We talked about last Friday's game being one of the most enjoyable games to watch this season for the Cardinals. Last night, save for one terrible inning, possibly rivaled it.
We said yesterday that Jeff Locke had had his struggles and the Cardinals had done well against him, but I don't think any of us expected this kind of carnage. Four runs in the first inning. Seven more in the third. Locke's line was even worse than some of Adam Wainwright's this season. 2.2 innings, 11 hits, 11 earned runs. He walked four, including Jedd Gyorko with the bases loaded, and only struck out two. In other words, he got eight outs and allowed 15 base runners. To say they had his number is probably an understatement.
What was somewhat sad, from a basic humanity point of view, is that it took run nine or 10 to get the Marlins' bullpen stirring. I get why that was, that sometimes you need a starting pitcher to wear one to save some arms later on. However, Locke wasn't actually getting any outs. He was at 84 pitches before he was done. The bullpen wound up covering about as much as they would have had he left after 5-6 runs. Of course, you don't know that if you are Don Mattingly, you hope that he can somehow at least get the Cards to hit it at someone, but Miami had to go to the pen early after all.
Now, here's what doesn't make much sense in my mind. The Marlins, whose starter left in the third, used four pitchers to finish off the game. The Cardinals, who had a significant lead and their starter pitched five innings, also used four pitchers to finish off the game. There was no reason for St. Louis to use that many arms to finish up what started as a five run lead and wound up to be eight. Luke Weaver made his season debut on a night that he was supposed to start for Memphis, yet went only one inning. It wasn't the best inning–two leadoff hits and the last out was a rocket off the bat of Giancarlo Stanton–but he only threw 20 pitches and didn't allow any runs. I would think that even with two innings he would be able to come back soon. Or you could let him go the distance and save a bunch of other arms.
Instead, Mike Matheny went to Tyler Lyons for the seventh. Again, Lyons is a guy that can go more than one frame. He's got a three inning save already this season. Lyons had plenty of movement on his pitches–which is why he wound up walking two as well as striking out two–and could have gone another frame. Maybe he would have if it wasn't for his spot coming up in the order, though if Matheny had thought he wanted him for an extra frame he'd have made a double switch. Heck, it's somewhat surprising he didn't make one anyway.
Then, for the eighth up 12-6, Matheny sends out Matthew Bowman again, because it'd been a day since he'd been on the mound. There's no doubt Bowman has pitched better of late, but there's still no reason to go back to overworking him. Sam Tuivailala took the ninth, which works given the situation. Like I say, I'm sure it was Matheny not wanting to limit future options by throwing a guy more than one inning and making him off-limits for a couple of days, but it really seemed excessive when you had such a big lead.
There was also the big day from Luke Voit, who got his first career RBI early in the game, then wound up with four of them after he cracked his first big league homer, a two-run shot, in the eighth. (I was off Twitter so I didn't get to tweet: "No, Jarlin Garcia, Luke is your father.") It's fun to see the big guy contributing. I don't know how long it'll last, but I imagine Voit's going to remain a fan favorite as long as it does.
One thing I DID Tweet early on in the game was that I was very glad that they'd given Adam Wainwright a cushion. I wasn't sure he'd need it, but I was very glad it was there. Given how the game started, with a single and a 19 pitches, it didn't feel like it was going to be the strong outings that we'd seen out of the former ace of late or in general at home. However, he made it through four innings without much incident. The fifth, though, is why he is the Goat of the game.
Four straight hits to open the inning brought in the first two runs. Waino then gets the top of the lineup, Dee Gordon and Stanton, to make it appear that things are under control. Not so much. A walk to load the bases, then back to back doubles that allow four to score. Suddenly it's 11-6, which is not the way things were supposed to go.
That said, I can't quite get on Matheny for leaving him in there, as some folks did. There are two outs. It's still a five run lead and you've gone through the heart of the order. I've already complained about how Matheny used so many arms in this game that he didn't need to. Letting Wainwright finish, especially when the lead wasn't currently in jeopardy, would have been the move I made as well. Yes, it could have come back to bite the Cardinals, but so often in these kind of games, there's one big push by the other team, then things return to normal. Which is what happened here–the Marlins didn't score again all evening.
The Brewers and the Cubs won, so the Cardinal victory just brought them closer to .500 but not to the division. The Cardinals will try to climb to one game shy of that even-water mark for the first time since June 3. Lance Lynn goes for the Cardinals, Jose Urena for the Marlins. Lynn did throw a quality start last time out but June was an ugly month for him. No matter what his future holds, whether he remains a Cardinal or is on the trade market, Lynn would do himself a great service to get back on track. Facing the Marlins may help with that.
Urena, on the other hand, seems to be having a fine season overall but stumbled last time out against the Mets, allowing five runs (three earned) in six innings. In the four starts before that, however, he had a 2.35 ERA, even if he never got past the sixth inning. In fact, six innings is the max he's gone this season, so the Redbirds likely will see the Miami bullpen again this afternoon.
The little experience they have with Urena has been pretty positive, so maybe we can see a few fireworks in the daytime today. Happy Fourth of July to you all and let's hope for another Cardinal victory!


text:
\section{Introduction}

Vision Transformers (ViTs) that take advantage of the self-attention mechanism to model long-range dependencies of input images achieve impressive accuracy in main-stream computer vision tasks especially on large datasets compared to CNNs, and attract considerable attentions of researchers from both industry and academia \cite{ali2021xcit} \cite{zhou2021deepvit} recently. Existing ViTs works mainly optimize performance and accuracy\cite{touvron2021going}\cite{ham20203}, but reliability induced by hardware faults that are almost inevitable in large-scale VLSI circuits remains overlooked generally. Reliability of deep learning models is of vital importance to both safety-critical applications like autonomous driving in which reliability can even hinder the adoption of the models in practice, and non-critical applications\cite{reagen2016minerva} like speech recognition in which reliability can be leveraged to achieve higher performance and energy efficiency.

The reliability of CNNs has been explored comprehensively in prior works\cite{FTT-NAS2020ASPDAC}\cite{Khoshavi2020DAC}\cite{liu2022special}, but there is still a lack of reliability analysis of ViTs that are constructed with Transformer blocks and differ substantially from CNNs. Specifically, ViTs\cite{liu2021swin}\cite{dosovitskiy2020image} with the self-attention mechanism usually incorporate global information of inputs while CNNs\cite{he2016deep} mostly extract local features with sliding window computing over inputs. Accordingly, hardware faults on inputs of ViTs become less significant and pose less influence on the model outputs. In addition, ViTs involve more complex non-linear functions like GELU and softmax in each layer while CNNs usually have simpler non-linear functions in a layer. Basically, non-linear functions of ViTs take up much higher proportion of the entire model computing. As a result, hardware errors induced computing variations in non-linear functions become non-trivial in ViTs and comprehensive reliability analysis of ViTs remains highly demanded.

To understand the influence of hardware faults on ViTs, we explore the vulnerability of ViTs in different granularities including models, layers, modules, and patches. The vulnerability factor of a ViTs component is defined to be the model accuracy increase between accuracy of model without protection and accuracy of model with the component under evaluation perfectly protected. It also reveals the model accuracy penalty if the target component suffers hardware faults. In this work, we adopt an operation-wise fault injection framework\cite{xue2022winograd} to obtain the vulnerability of different components of ViTs. Based on the vulnerability analysis of ViTs, we further propose corresponding fault-tolerant approaches to protect the linear functions including GEMM and FC, and non-linear functions including GELU and softmax respectively. For the linear functions of ViTs that are generally implemented with GEMM, we propose an adaptive algorithm-based fault tolerance (ABFT) approach to suit the GEMM with different sizes in ViTs. For the non-linear functions, we apply a range-based protection approach to avoid acute computing results and alleviate the negative influence of hardware faults. The proposed fault-tolerant approaches show significant model accuracy improvement with moderate computing overhead. The major contributions are summarized as follows:

\begin{itemize}
\item We perform a comprehensive reliability analysis of ViTs from different granularities including models, layers, modules, and patches in presence of various soft errors for the first time.

\item With the reliability evaluation, we observe that linear functions in ViTs are more resilient than that in CNNs but the non-linear functions in ViTs pose more negative influence on the model accuracy in presence of hardware faults. In addition, vulnerability factors across the inputs of ViTs are generally evenly distributed, which differs substantially from that of CNNs.

\item Based on the reliability analysis, we propose an adaptive ABFT approach and a range-based method to mitigate hardware faults in linear functions and non-linear functions of ViTs respectively. Our experiment results show that the hybrid approach generally enhances the model accuracy significantly and particularly reduces the computing overhead considerably when compared to the baseline global ABFT implementation.

\end{itemize}

\section{Background and Related Work}

\subsection{Vision Transformers}
Inspired by the great success of the Transformer architecture in natural language processing (NLP), researchers also applied Transformer to computer vision tasks including classification, detection, segmentation, and demonstrated superior performance of ViTs \cite{dosovitskiy2020image}. A typical ViT architecture as presented in Fig.\ref{fig:vit-arch} is composed of multiple Transformer encoders and each encoder consists of a set of connected modules including multi-head attention module (MHA), feed-forward module (FF), residual connections, and layer normalization. MHA is utilized to model long-range dependencies of inputs and mainly includes GEMM, FC, softmax, and some simple layers like scale and concat. FF consists of two linear FC layers and a nonlinear activation function i.e. GELU. As for the residual connections and normalization, they are the same with that in conventional CNNs. In addition, ViTs need to split input images into a sequence of non-overlapped patches with linear projection.

\begin{figure}[!t]
\centering
\includegraphics[width=0.47\textwidth]{./img/vit.pdf}
\vspace{-1em}
\caption{Typical Vision Transformer Architecture.}
\label{fig:vit-arch}
\vspace{-1em}
\end{figure}

\subsection{Reliability Analysis of Deep Learning}
Soft errors that are almost inevitable in existing large-scale VLSI designs typically manifest as bit flips and can propagate along with the deep learning data flow, which can cause incorrect computing results and induce considerable prediction accuracy loss of deep learning models. Quantifying the influence of soft errors and understanding the reliability of deep learning is an essential step to protect against the soft errors especially for safety-critical applications like autonomous driving and robotics. There have been many prior works that evaluated the reliability of CNNs subjected to soft errors from different angles. For example, \cite{li2017understanding} studied the resilience of CNNs with different data types, values, data reuses, and types of layers to guide the fault tolerance design. \cite{reagen2018ares} explored the relationship between fault rate and model accuracy under various setups such as quantization, layer type, and network structure. Some of the reliability evaluations further explored the reliability difference among components of deep learning models such that they can be utilized to perform selective protection using less protection overhead  \cite{mahmoud2021optimizing} \cite{ping2020sern}. More reliability evaluation works of deep learning can be found in recent surveys  \cite{mittal2020survey} \cite{liu2022special}.

\subsection{Fault Tolerance of Deep Learning}
To protect deep learning processing against errors in silicon, many fault-tolerant approaches have been extensively studied. Classical redundancy approaches such as triple modular redundancy (TMR) can potentially alleviate the influence of soft errors, but straightforward TMR requires substantial computing overhead and they are usually combined with vulnerability analysis such that fragile components can be selectively protected to reduce the overhead \cite{R2F2021TVLSI}. ABFT techniques \cite{huang1984algorithm} which are originally developed for matrix-matrix multiplication based on checksum have also been explored to detect and recover faults in deep learning without model modification, which are particularly suitable for commercial-off-the-shelf computing engines. Different from the above approaches that usually keep deep learning models intact, many prior approaches explored the inherent fault tolerance of deep learning models with distinct strategies such as model architecture design \cite{FTT-NAS2020ASPDAC}, model parameter retraining \cite{Khoshavi2020DAC}, and quantization restriction \cite{chen2021low}.

\section{ViTs Reliability Evaluation} \label{sec:reliability-evaluation}
We mainly quantify the reliability of ViTs from the perspective of network architectures and investigate the vulnerability of ViTs from different granularities including models, layers, modules, and patches respectively.

\subsection{Evaluation Setups}
The major experiment setups include datasets, ViTs models benchmark, fault models, and fault injection framework.

\textbf{Dataset.} We take the well-known ImageNet-1K as the dataset \cite{deng2009imagenet} and randomly select 10000 images for the accuracy evaluation.

\begin{table}[htb]
\caption{Major Parameters of the ViTs benchmark\label{tab:table1}}
\centering
\begin{minipage}{\textwidth}
\begin{tabular}{c c c c c}
\hline
Network & Layer Num & Head Num & eDim\footnote{eDim refers to embedding dimension.} & Patch Size \\
\hline
ViT-B & 12 & 12 & 768 & $16\times16$\\
Swin-T & [2, 2, 6, 2] & [3, 6, 12, 24] & 96 & $4\times4$\\
DeepViT-S & 16 & 12 & 396 & $16\times16$\\
CaiT-XXS-24 & 24+2 & 4 & 192 & $16\times16$\\
ResNet18 & 20 & - & - & -\\
\hline
\end{tabular}
\end{minipage}
\end{table}

\textbf{ViTs Models.} We select four representative ViTs models as the benchmark and ResNet18\cite{he2016deep} for comparison. The configurations of these models are summarized in Table \ref{tab:table1}. ViTs include ViT-B\cite{dosovitskiy2020image}, Swin-T\cite{liu2021swin}, DeepViT-S\cite{zhou2021deepvit}, and CaiT-XXS-24\cite{touvron2021going}. All the deep learning models are quantized with int8. Top-1 accuracy is utilized as the evaluation metric.

\textbf{Fault Models.} We utilize bit flip model to characterize typical soft errors. Particularly, following prior reliability analysis work \cite{xue2022winograd}\cite{li2017understanding} \cite{reagen2018ares}, we also use bit error rate (BER), which represents the ratio of bit flip errors over the total number of bits of operations in the model as the soft error intensity metric. It ranges from 0 to 1E-7 which can cover the major model accuracy degradation curve.

\textbf{Error Injection.} We adopt an operation-wise error injection method proposed in \cite{xue2022winograd} for the reliability evaluation. It is built on top of PyTorch and injects random bit flip errors to outputs of the major arithmetic operations i.e. multiplication and addition in each model, which focuses on the computing of the models rather than a specific computing engine. It covers both linear and non-linear functions in the models.

\textbf{Vulnerability Factor.} To differ the reliability of different ViTs components, we utilize vulnerability factor as the metric and it refers to the model accuracy difference between model with only the target component perfectly protected and the model without any protection.

\textbf{Hardware Platforms.} All the evaluation experiments are performed on a server equipped with two 24-core@2.5GHz Intel Xeon processors, 512GB memory, and four PH402 SKU 200 GPU cards.

\begin{figure*}[!t]
\centering
\includegraphics[width=0.95\textwidth]{./img/model_wise.pdf}
\vspace{-1em}
\caption{Top-1 model accuracy at different BER setups.}
\vspace{-1em}
\label{fig:network-acc}
\end{figure*}

\subsection{Model Reliability Evaluation}
We evaluated the Top-1 accuracy under different BER to characterize the resilience of the models. As shown in Fig.\ref{fig:network-acc}, curve is generally steady when the BER is low, but it drops sharply when the distributed errors reach certain threshold at higher BER. In addition, although the general trend of the accuracy curves of different models are similar, model architecture poses substantial influence and results in different accuracy degradation. Specifically, the model accuracy of ViTs starts to drop at much higher BER than ResNet18. Enhanced ViTs models including Swin, DeepVit, and CaiT are more resilient and drops relatively slower compared to ViT-B.

\begin{figure*}[!t]
\centering
\includegraphics[width=0.95\textwidth]{./img/layer.pdf}
\vspace{-1em}
\caption{Layer vulnerability factors.}
\vspace{-1em}
\label{fig:layer-reliability}
\end{figure*}

\subsection{Layer Reliability Evaluation}
We take Transformer blocks as layers, and investigate layer vulnerability factors in this sub section. Since vulnerability factors is closely related with BER, we set BER to be the value that leads to moderate accuracy loss i.e. 2\% to 10\%, which can be potentially addressed with reasonable overhead. According to Fig.\ref{fig:network-acc}, we set BER to be 9E-11, 5E-10, 5E-10, 4E-10, and 2E-11 for ViT-B, Swin-T, DeepViT-S, CaiT-XXS-24, ResNet18 respectively. The experiment results as presented in Fig.\ref{fig:layer-reliability} reveal that the vulnerability factors vary across the layers, but the variation over ViTs layers is relatively smaller than that over ResNet18, which is probably caused by the global feature extraction based on the self-attention mechanism in ViTs. For ViTs, vulnerability factors of the first few layers are higher and more sensitive to the errors, because errors in upstream transformer layers propagate along the model and pose more negative influence than the later layers. The only exception is CaiT. This can be attributed to the LayerScale technique proposed in CaiT which has a learnable diagonal matrix multiplied to outputs of each transformer block and scales down the output values substantially. As a result, the error induced computing variation in front layers is suppressed and contributes less to the final model accuracy. In contrast, the last few layers of CaiT models without the LayerScale operation are more fragile.

\subsection{Module Reliability Evaluation}
We also explore the reliability of ViTs from the perspective of operation modules. ViTs can be roughly divided into linear functions such as GEMM and FC, and non-linear functions (NLF) such as softmax and GELU. Particularly, we have linear functions in MHA and FF separated to MHA-LF and FF-LF respectively. In contrast, ResNet is divided into convolution modules (Conv), FC modules, NLF modules like ReLU. BER setups are the same with that used in layer reliability analysis.

The evaluation in Fig.\ref{fig:module-wise} reveals that vulnerability factors vary substantially across the different modules. NLF that takes up only a fraction of the overall operations of ViTs turns out to be the most fragile part. As for ResNet18 with much less NLF, vulnerability factors of NLF remains non-trivial, which confirms the importance of NLF on the overall model reliability. In contrast, vulnerability factors of ViTs linear functions that involve a large number of operations are relatively lower. This is mainly because that the NLF especially softmax limit the output value within the range between 0 to 1 and suppresses the influence of errors substantially. For ResNet18, the vulnerability factors of linear functions i.e. Conv and FC are significantly higher as expected because they take up the majority of operations in ResNet18.

\begin{figure}[!t]
\centering
\includegraphics[width=3.5in]{./img/module.pdf}
\vspace{-2em}
\caption{Module vulnerability factors.}
\vspace{-1em}
\label{fig:module-wise}
\end{figure}

\subsection{Patch Reliability Evaluation}
In this section, we investigate the vulnerability factors of different patches in ViTs. To highlight the difference across the patches, we set BER to be 1E-4, 3E-4, 3E-4, and 1E-4 for ViT-B, Swin-T, DeepViT-S, and CaiT-XXS-24 respectively. The vulnerability factors are presented with a heatmap as shown in Fig.\ref{fig:patch-wise}. It shows that the distribution of the vulnerability factors of ViTs and CNNs are quite different. Since objects in ImageNet are usually located in the center of input images, errors on pixels located in the center pose more negative influence on CNN model accuracy as expected. The patch vulnerability factors of ViTs show a relatively even distribution and vulnerable patches can scatter across the input images. This is because that ViTs leverage the self-attention mechanism to obtain global information rather than local features.

\begin{figure*}[!t]
\centering
\includegraphics[width=0.95\textwidth]{./img/patch.pdf}
\vspace{-1em}
\caption{Heatmap of patch vulnerability factors.}
\vspace{-1em}
\label{fig:patch-wise}
\end{figure*}

\section{Fault-Tolerant Approaches for ViTs}
According to the analysis in Section \ref{sec:reliability-evaluation}, we notice that linear functions that are typically implemented with GEMM take up considerable computing overhead but are generally resilient to soft errors, so they need to be protected with a lightweight approach. NLF involves much less computing but poses dramatic influence on the model reliability, so NLF protection needs to emphasize more on the accuracy rather than computing overhead. In this work, we propose an adaptive yet lightweight ABFT approach to protect the linear functions in ViTs and we adopt a range-based approach to explicitly suppress the outputs of NLFs that can vary considerably in presence of bit-flip errors. Details about the fault-tolerant approaches will be illustrated as follows.

\subsection{Fault-tolerant Approach for Linear Operations}
ABFT algorithm essentially leverages a checksum mechanism to obtain sum of outputs with different approaches such that they can be utilized for both the fault detection and recovery. ABFT can be divided into an error detection stage and an error recovery stage. Take an $n \times n$ GEMM as an example, error recovery requires $2n^2$ MACs and it is more expensive compared to error detection that requires only $n$ MACs. Error recovery is invoked only when computing errors are detected, but it is beneficial only when there is only one output error in each row or column due to the limited error correction capability. Thereby, it is inefficient to adopt ABFT when multiple computing errors occur in a single row or column at higher BER. In summary, ABFT quality from the perspective of fault tolerance depends on the GEMM sizes and BER. Meanwhile, layer vulnerability factors can vary, which further complicates the ABFT-based ViTs protection.

To address the problem, we propose an adaptive ABFT strategy that can split GEMM into smaller blocks to suit the fault tolerance requirements of GEMM with distinct sizes across the ViTs layers. Specifically, we utilize the layer vulnerability factors to quantify the fault-tolerance requirements of each layer and adjust the ABFT blocking size to meet the accuracy requirements and minimize the computing overhead. In addition, we also set a computing overhead limit to avoid the excessive use of ABFT at higher BER. Specifically, given BER, target model accuracy, and computing overhead limit, we apply ABFT to the most vulnerable layer and update the vulnerability factor of the protected layer. Then, we repeat the procedure continuously until the target model accuracy is achieved. When a layer is selected more than once, the GEMM is split into more blocks and ABFT is applied to each sub block. When the overall computing overhead exceeds the computing overhead limit, we optimize the model accuracy under the computing overhead limit. The accuracy optimization is similar to the above overhead optimization procedure and it mainly swaps the design goal and design constrain. At the end of this algorithm, we can obtain optimized ABFT strategies for ViTs under various BER setups.

\subsection{Fault-tolerant Approach for Non-Linear Operations}
For NLFs in ViTs, we adopt a range-based approach to suppress the hardware faults induced computing errors in general. Specifically, for NLFs with determined range such as softmax, outputs that exceed the range between 0 and 1 will be set to be zero. For the NLFs without fixed range, we set the range to be $((1+\alpha) \times min, (1-\alpha) \times max)$ where $min$ and $max$ represent the minimum and maximum values of the function outputs respectively and they can be obtained through statistic with a set of sampling inputs. $\alpha$ is utilized to filter out large outputs with very limited number and it is set to be 0.02 according to \cite{chen2021low}. Similarly, computing results that are out of the range will be set to be zero. Since the range-based approach only needs comparison operations and its overhead is trivial compared to ViTs processing.

\subsection{Fault-Tolerant Design Evaluation}
The proposed adaptive ABFT and range-based method are applied to protect ViTs against various soft errors. The accuracy loss relative to a clean model is set to be less than 2\% and the maximum computing overhead is set to be 2\% normalized to the total ViTs computing. Note that we utilize the number of multiplication operations as the computing overhead metric. The experiment shown in Fig.\ref{fig:pro_linear_nolinear_acc} reveals that the proposed approach enhances model accuracy significantly in general and the accuracy can be almost fully recovered even when the accuracy loss of the unprotected models goes up to 50\% at relatively lower BER. When BER is higher, more computing errors will occur and error recovery will be invoked more frequently, which can induce considerable computing and fails the computing overhead limit. In this case, we opt to relax the model accuracy requirement by using larger ABFT blocks and skipping some of the error recovery to fulfill the computing overhead limit and avoid excessive use of ABFT. Thereby, the model accuracy drops more clearly as expected. For the range-based protection, its overhead stays the same at different BER, but its benefit to accuracy increases with BER, because NLFs are usually placed after linear functions and also help to suppress the computing errors of linear functions especially when ABFT fails to correct the computing errors at higher BER.

ABFT requires multiplication, addition, and comparison while range-based approach only requires comparison. The computing overhead of the protection approaches is presented in Fig.\ref{fig:pro_linear_nolinear_flops}. It reveals that the computing overhead of baseline ABFT grows rapidly with BER because more expensive error recovery is required at higher BER. However, the model accuracy still drops dramatically at higher BER due to the limited fault recovery capability. In contrast, the proposed adaptive ABFT adjusts the blocking sizes to suit the different vulnerability factors of layers as well as BER, which reduces the computing overhead substantially and achieves higher model accuracy. In addition, the higher accuracy is also attributed to the fact that we set the faulty outputs to be zero rather than the original faulty data which poses less negative influence on accuracy as well. Note that NLFs are set to be fault-free to highlight the ABFT analysis in Fig.\ref{fig:pro_linear_acc}.

\begin{figure*}[!t]
\centering
\includegraphics[width=0.85\textwidth]{./img/pro_linear_nolinear_acc.pdf}
\vspace{-1em}
\caption{Model accuracy of ViTs with different fault-tolerant approaches.}
\vspace{-1em}
\label{fig:pro_linear_nolinear_acc}
\end{figure*}

\begin{figure*}[!t]
\centering
\includegraphics[width=0.85\textwidth]{./img/pro_linear_nolinear_flops.pdf}
\vspace{-1em}
\caption{The computing overhead of different fault-tolerant approaches.}
\vspace{-1em}
\label{fig:pro_linear_nolinear_flops}
\end{figure*}

\begin{figure*}[!t]
\centering
\includegraphics[width=0.85\textwidth]{./img/pro_linear_acc.pdf}
\vspace{-1em}
\caption{Model accuracy with different ABFT implementations.}
\vspace{-1em}
\label{fig:pro_linear_acc}
\end{figure*}

\section{Conclusion}
In this paper, we perform comprehensive reliability analysis of ViTs at different granularities including models, layers, modules, and patches, and reveal the reliability difference compared to CNNs. Based on the analysis, we proposed an adaptive ABFT approach to adjust blocking size to fit the reliability requirements of different ViTs linear functions and BER setups, and applied a range-based approach to protect NLFs that are generally overlooked. According to our experiments, the proposed adaptive ABFT achieves great advantages on both accuracy and computing overhead over the baseline fixed ABFT at various BER setups. The hybrid approach shows significant accuracy improvement and can almost fully recover the accuracy even when the accuracy drops by 50\%.

\bibliographystyle{ieeetr}

redpajama_set_name: RedPajamaArXiv


text:
200 Okatie Village Drive
How do you define the ultimate cycling experience? That's up to you. Our mission at Giant is to help make it happen. To create the ultimate cycling experience for all riders, all around the world.How do you define the ultimate cycling experience? That's up to you. Our mission at Giant is to help make it happen. To create the ultimate cycling experience for all riders, all around the world.
We are builders and innovators, but we are also a global community of cyclists. We are athletes, adventurers and advocates for cycling. We are Tour de France racers, singletrack explorers, neighbors and friends.
It's true that Giant is the world's largest producer of high-quality bikes, but we never forget where we came from. We started small. And we were founded on the idea that the best way to inspire passion for cycling is to create the best products, and make them accessible to all riders. That's what we did back then. That's what we do today.
Much has changed since we started in 1972. In fact, Giant has long been one of cycling's main catalysts for change. We introduced lighter, stronger aluminum frames at a time when the industry standard was steel. We were first to make carbon fiber bikes widely available to the world. We defined the look and feel of modern road racing bikes with our Compact Road technology. And we revolutionized off-road performance bikes with Maestro Suspension.
Today, this spirit of innovation is stronger than ever. Our collection of gear and apparel, developed and tested by some of the top teams and athletes in pro racing, continues to expand. Our industry-leading E-bikes are redefining what's possible for riders of all abilities.
These innovations serve a larger purpose. It's not just about winning races and producing lighter, stronger, faster bikes. That's part of it, but it's also about making cycling more enjoyable and approachable. To everyone.
When we talk about "Ride Life," this is what we mean. Our products, our people, our passion. Our commitment to enhancing your cycling life. You can find us at our more than 12,000 retail stores around the world, or connect with us online from wherever you are. We are always accessible and never too far away.
Ride Life. Ride Giant.
Call 843-815-8281 or Email
Sports Addiction Request
Email Sports Addiction
sportsaddiction843@gmail.com
Copyright © 2019. Sports Addiction Bluffton Bikes & Fitness. Designed by DC Design

redpajama_set_name: RedPajamaCommonCrawl


There is nothing like intimidation to inspire one!
This entry was posted in Life, society and tagged challenges, choices, daily quip, intimidation, perception on September 4, 2018 by quiall.
If it walks like a jerk, and it talks like a jerk, then . . . well, I didn't make the rules!
This entry was posted in Life, society and tagged attitude, conversational comebacks, humour, intimidation, Shady Quip on June 22, 2018 by quiall.
Just because something is hard to do, doesn't mean it shouldn't be done.
This entry was posted in Life, society and tagged adapting, adventure, daily quip, intimidation, positive changes on April 12, 2018 by quiall.


text:
Age of Empires II: The Age of Kings () — видеоигра в жанре стратегия в реальном времени, разработанная Ensemble Studios и выпущенная Microsoft. Является второй игрой из серии Age of Empires. Была выпущена в  году, а год спустя увидело свет дополнение The Conquerors, содержащее новые сюжетные линии, игровые цивилизации и усовершенствованный игровой процесс.

События оригинальной The Age of Kings происходят в средних веках, а количество доступных для игры цивилизаций равняется 13. Игроку требуется собирать ресурсы, отстраивать города и создавать армии, при помощи которых он должен одолевать противников. Имеется пять кампаний, основанных на исторических событиях, в которых игрокам ставятся определённые условия, связанные с сюжетом и необходимые для победы. Помимо них, имеется три дополнительных режима одиночной игры, а также мультиплеер.

9 июня 2019 года было анонсировано, а 14 ноября 2019 официально выпущено второе переиздание игры под названием Age of Empires II: Definitive Edition. Оно включает в себя перерисованную графику в разрешении Ultra HD 4K, записанный заново саундтрек, а также дополнение The Last Khans с 3 новыми кампаниями.

Игровой процесс
Действие игры происходит в реальном мире, условно охватывая период Средневековья, начиная с падения Римской империи и заканчивая Эпохой Возрождения. Игрок управляет одной из 16 (со всеми дополнениями — 37) представленных народностей, добывая ресурсы и отстраивая поселения с помощью рабочих, развивает экономику, изучает новые технологии и создаёт армию. Основная цель игры — уничтожить своих соперников, которые параллельно развивают свою цивилизацию на близлежащей территории.

В игре присутствует довольно развитая экономическая система. Имеются 4 вида ресурсов — древесина, пища, золото и камень.

Древесина используется в основном для постройки зданий, создания лучников, осадных орудий, флота, а также для постройки ферм, которые в свою очередь производят пищу. Добывается он путём рубки деревьев и складирования на ближайшей лесопилке. Большинство карт имеют более чем достаточное количество этого ресурса, причём рассредоточен он по карте относительно равномерно.

Пища — пожалуй, самый востребованный ресурс в игре. Она необходима для постройки рабочих, для создания пехоты и конницы, для большинства апгрейдов для войск. Также очень большое количество пищи требуется для перехода в вышестоящие эпохи — ключевых апгрейдов, необходимых для создания зданий и юнитов нового типа. Добываться может многими способами. На начальных этапах игры в основном добывается путём охоты на различных животных (овцы, олени, кабаны), путём собирательства (ягодные кусты), также путём рыбалки при наличии на карте водоемов с рыбой. Рыбалка может вестись крестьянами у берега, либо на глубоководье с помощью рыбацких судов. На более поздних этапах эти ресурсы начинают иссякать, а пищи требуется все больше. Так игрок плавно переходит к постройке ферм, которые в достаточном количестве обеспечивают полностью автоматизированный и постоянный прирост этого ресурса.

Золото необходимо в основном для создания самых мощных боевых юнитов, а также для высокоуровневых апгрейдов. На начальных стадиях игры играет куда более скромную роль. Может добываться из рудников, точечно расположенных по всей карте, однако этот источник быстро иссякает, и тогда между игроками возникают настоящие схватки за оставшиеся месторождения. Также может производиться за счёт сухопутной или морской торговли при наличии союзных игроков.

Камень требуется для постройки замков, башен и каменных стен. Добывается единственным способом путём разработки рабочими месторождений, расположенных на карте аналогично золотым месторождениям, но в несколько меньшем количестве.

В игре имеется возможность менять ресурсы на рынке по определенному курсу. В начале игры стоимость ресурсов примерно одинакова, на поздних этапах золото скупается игроками в обмен на пищу, лес и иногда камень, что приводит к обесцениванию этих ресурсов (инфляция).

Как правило, игра начинается с тёмных веков, когда доступными игроку являются лишь самые простые здания и юниты. Каждый игрок имеет отстроенный городской центр, не менее трёх крестьян и конного разведчика. Первоначальные задачи — разведка близлежащей территории и добыча значительного количества еды, необходимого для непрерывного создания новых рабочих и последующего перехода в феодальную эпоху. Лес добывается в меньших количествах, золото и камень — опционально. Из юнитов доступны только рабочие, ополченцы (слабая пехота, создаётся в казармах) и рыбацкие суда (в доках).

Феодальная эпоха является ключевым моментом для выбора дальнейшей стратегии развития — либо строительство армии для немедленной атаки противника, либо быстрый переход в эпоху замков. Последнее может на время поставить игрока в уязвимое положение, зато даст значительные преимущества в будущем. Из военных юнитов становятся доступны лучники, метатели копья, лёгкая кавалерия и пикинёры, также возможно улучшение ополченцев до мечников.

Основное нововведение замковой эпохи — это возможность строить дополнительные городские центры, в которых можно будет в свою очередь одновременно создавать новых рабочих. Это даёт очень мощный толчок для развития экономики, однако требует поначалу немалых вложений. Важно правильно выбрать время для вливания инвестиций в экономику и не забывать при этом об обороне и нападении.

Наконец, возможен переход в последнюю, имперскую эпоху. Этот переход чрезвычайно дорогостоящий и требует много времени, так что совершается он при уже полностью налаженной экономике. Имперская эпоха позволяет совершать самые высокоуровневые апгрейды на армию, здания и экономику. Также существует «постимперская эпоха», условно наступающая по совершении игроком всех возможных апгрейдов.

В дополнении к оригинальной игре Age of Empires II: The Conquerors, а также в двух последующих, есть карты, представляющие территории из реального мира (Скандинавия, Центральная Америка, Японское море).

В игре с несколькими игроками действует дипломатия — они могут быть союзниками, сохранять нейтралитет или вести войну.

Многопользовательский режим поддерживает до 8 игроков.

Переиздание

Age of Empires II HD
7 марта 2013 года Microsoft анонсировала переиздание игры в HD. Поскольку компании-разработчика оригинальной игры, Ensemble Studios, больше не существует, работу над переизданием осуществила компания Hidden Path Entertainment (разработчик Counter-Strike: Global Offensive). Новая версия игры поддерживает разрешения экрана 1920x1080 и более, а также последние версии Windows и DirectX. Графический «движок» игры доработан, а текстуры огня, воды и земли переработаны с учетом увеличенного разрешения. Пространству игры также добавлено динамическое освещение. Игра поддерживает функции Steam, такие как: достижения, таблица лидеров, многопользовательский режим, Steam Cloud, Steam Workshop.

В игре присутствуют все кампании из оригинальной игры, а также дополнительные кампании и особенности из дополнения The Conquerors.

Переизданная версия игры доступна эксклюзивно в Steam c 9 апреля 2013 года. Пользователям, сделавшим предварительный заказ, — с 5 апреля.

Летом 2018 года, благодаря уязвимости в защите Steam Web API, стало известно, что точное количество пользователей сервиса Steam, которые играли в игру хотя бы один раз, составляет .

16 августа 2019 года название Steam-версии игры было изменено на Age of Empires II (2013) в связи с выходом переиздания Age of Empires II: Definitive Edition.

Age of Empires II HD: The Forgotten
Дополнение для Age of Empires II, выпущенное только для «HD Edition» версии игры и доступное через Steam. Разработчиком является Forgotten Empires LLC при участии Skybox Labs. The Forgotten добавляет в игру пять новых цивилизаций (итальянцы, индийцы, славяне, венгры и инки), четыре новые кампании, новые постройки и войска, новые улучшения, новые режимы игры, новые карты, улучшенный ИИ.

Age of Empires II HD: The African Kingdoms
Второе дополнение для Age of Empires II: HD Edition, доступное через Steam. Разработчиком является Forgotten Empires LLC при участии Skybox Lab. The African Kingdoms добавляет в игру четыре новых цивилизации (берберы, малийцы, эфиопы, португальцы), четыре новых кампании — за арабского полководца берберского происхождения, завоевавшего королевство вестготов, Тарика ибн Зияда, основателя империи Мали Сундиата Кейта, португальского вице-короля Индии Франсишку ди Алмейда и царицу эфиопских евреев Юдит. Добавлены новые юниты и технологии (бойницы (увеличивает мощность башен), поджог (увеличивает урон по зданиям пехоте), огненная галера; осадная башня), новые карты и режимы игры, интеграция с Twitch.tv.

Age of Empires II HD: Rise of the Rajas
Третье дополнение для Age of Empires II: HD Edition, доступное через Steam. Разработчиком является Forgotten Empires LLC при участии Skybox Labs. Rise of the Rajas добавляет в игру четыре новых цивилизации (бирманцы, кхмеры, малайцы, вьетнамцы), четыре новых кампании, а также два новых юнита, улучшенный ИИ и режим наблюдателя.

Кампании
Оригинальная игра включает пять кампаний, Age of Empires II: The Conquerors — ещё четыре.

Кампания состоит из нескольких сценариев с возрастающей сложностью. В их основе лежат события из жизни исторических персонажей. В начале кампании игрок получает некоторое количество ресурсов и юнитов. В оригинальной игре есть кампании за Уильяма Уоллеса, Жанну Д'Арк, Саладина, Чингисхана, Барбароссу. В The Conquerors добавляются кампании за Аттилу, Эль Сида и Монтесуму, а также присутствует кампания, посвящённая средневековым битвам. Дополнение The Forgotten позволяет играть за Алариха, Дракулу, прибывающую в Бари семью Наутикос, Сфорцу, ищущих Эльдорадо Писарро и де Орельяну, Притхвираджа и кампания «Битвы забытых героев». В The African Kingdoms есть кампании за Тарика ибн Зияда, Сундиату, Франсишку ди Алмейда и Юдит. Дополнение Rise of the Rajas добавляет кампании за Гаджа Маду, Сурьявармана, Байиннауна и Ле Лоя. Во втором переиздании в рамках дополнения The Last Khans созданы новые кампании за Ивайло, Тамерлана, Котяна Сутоевича и «Искусство войны», которая позволяет опробовать свои навыки игры. Также была убрана кампания «Эльдорадо», вместо неё появились миссии за Пачакути. В дополнении «Lords of the West» добавились кампании за Эдуарда Длинноногого, герцогов Бургундии и династию Готвилей

Цивилизации
В игре изначально 13 цивилизаций: британцы, византийцы, викинги, готы, кельты, китайцы, монголы, персы, сарацины, тевтоны, турки, франки, японцы. Каждая цивилизация имеет уникального юнита (кроме викингов, которые имеют два уникальных юнита), уникальную технологию (у готов две технологии) и несколько уникальных бонусов.

В Age of Empires II: The Conquerors добавлены 5 цивилизаций (ацтеки, гунны, испанцы, корейцы, майя).

В Age of Empires II HD: The Forgotten добавлены 5 цивилизаций (венгры, индийцы, инки, итальянцы, славяне).

В Age of Empires II HD: The African Kingdoms добавлены 4 цивилизации (берберы, малийцы, эфиопы, португальцы).

В Age of Empires II HD: Rise of the Rajas добавлены 4 цивилизации (бирманцы, кхмеры, малайцы, вьетнамцы).

В Age of Empires II Definitive Edition: The Last Khans добавлены 4 цивилизации (болгары, половцы, литовцы, татары).

В Age of Empires II Definitive Edition: Lords of The West добавлены 2 цивилизации (бургундцы и сицилийцы).

В Age of Empires II Definitive Edition: Dawn of the Dukes добавлены 2 цивилизации (поляки и чехи)

В Age of Empires II Definitive Edition: Dynasties of India добавлены 3 цивилизации (бенгальцы, гурджары и дравиды)

Технологии
Дерево технологий в игре более сложное, чем в первой части. Чтобы воспользоваться технологией, игрок должен сначала построить здание. В каждом здании можно разработать технологию за определённую цену. После разработки технологии появляется более широкий выбор юнитов и новых технологий. Благодаря технологиям можно развить юниты, крестьян и здания. Выбор технологий шире в более высокой Эпохе. У каждой цивилизации есть уникальная технология. Например, у франков стоимость замка меньше, чем у других цивилизаций. Но есть также и недостатки: так, у персов меньшая сила атаки против осадных орудий (баллист, таранов, катапульт). Не надо забывать, что технологическое развитие базируется на экономическом развитии. Поэтому цель игры также в том, чтобы превосходить противника и в экономическом плане.

Чудеса света и реликвии
В игре имеются небольшие предметы — реликвии, которые может подбирать только монах. (Конный монах из Age of Empires II: The Conquerors не может собирать реликвии.) Если реликвию поместить в монастырь, то она приносит золото игроку. Противник может отвоевать реликвию, разрушив монастырь. Сбор всех 5 реликвий и удержание их в течение определённого времени приводит к победе.

Можно победить, собрав все реликвии или построив Чудо света. Чудо света — большое здание, на постройку которого уходит много ресурсов и времени. Если Чудо света простоит определённое время, то игрок побеждает. О начале строительства Чуда уведомляются все игроки, расположение Чуда открывается на карте. У разных цивилизаций — разные Чудеса, но на их функции это не влияет.

Примечания

Ссылки
 IGN: Top 25 PC Games of All Time (страница 2 и 3)
 Официальный веб-сайт
 Как играть по сети

Стратегии в реальном времени
Компьютерные игры, изданные Xbox Game Studios
Компьютерные игры с изометрической графикой
Игры для Windows

redpajama_set_name: RedPajamaWikipedia


Today Magners announced that, following unprecedented demand, their 1967 limited edition pack has sold out in less than a fortnight. But fans need fear not – another run of the Lisbon Lions special cider has been ordered and will be available in the coming weeks.
Celtic sponsors, Magners have raised a staggering £19,670 for the Celtic FC Foundation through the special pack. £10 from each of the first 1,967 packs was donated to employability programs that help long-term unemployed people get back into work.
The commemorative packs of Magners Original Cider were emblazoned with the iconic image of the Celtic team taking to the pitch ahead of the 1967 Final, with each bottle label carrying the names of the legendary players who made history in the heat of Lisbon.
The special packs, which mark the 50th anniversary of the Hoop's historic win, will be available again from the end of June and fans looking to order can reserve them from the Celtic Superstore in Glasgow (T: 0141 551 4231).


Calling All BibiNogs' Families, Come Celebrate Mother's Day with Us!
We would like to invite ALL BibiNogs' families to join us for a very special Mothers' Day party on 13th May, Saturday at 9.30am! Mummy, you are SPECIAL and you deserve a warm celebration with your families!
BibiNogs Director Ms. Joyce Lim's career transition and accomplishments was featured as a success story by WSG Workforce Singapore's Career Support Programme. Way to go!
Read on to get to know our Director better..


This study evolved from the realization that there was a distressing need among high school students for tangible help in reading .
As a result of this, I enrolled in the Butler University course of Clinical Practice in Reading, under Dr . Townsend, for definite help in meeting this necessity. He inspired me to make a survey of reading difficulties and do some experimental remedial reading in the school at which I was teaching, Arsenal Technical Schools, Indianapolis, Indiana.
Horne, Leunice H., "A Survey of the Reading Achievement in the 9B at Arsenal Technical Schools and Suggestions for a Remedial Program" (1940). Graduate Thesis Collection. 227.


Looking for Information technology in Ireland? Find the list of best Information technology Companies in Ireland on our business directory.
Really Good Business provides Managed IT Services and Managed IT Solutions to businesses throughout Dublin and Ireland.


All San Diego State University undergraduate students aged 23 and younger who have not been fully immunized with the meningococcal B (MenB) vaccine are strongly recommended to get vaccinated as soon as possible.
Fully immunized means having received all doses required for the MenB, a multi-dose series. For those who have received only one vaccine dose, it is important to complete the series to improve protection. Immunization is still recommended for all of those under age 24 who received Cipro(R) (ciprofloxacin) due to possible exposure as a close contact in early September.
Students should promptly contact their healthcare provider to explore MenB vaccination options.
Students with health insurance may contact a local pharmacy to receive the vaccine (call ahead to make sure the MenB vaccine is available and your insurance is accepted; if not accepted, call your insurance to find pharmacies that take your insurance).
Students can attend a vaccination clinic (details below) hosted by San Diego County Public Health Services and SDSU on Friday, October 5, and Monday, October 8.
Only enrolled undergraduate students aged 23 and younger who choose to be vaccinated will be allowed entry; students may bring a friend or family member for support, if desired. San Diego County Public Health Services will be on site to administer vaccines and will only be providing students with their first dose of the MenB vaccine.
Studies have shown that, among college students, rates of meningococcal infection are higher among those who live in on-campus residential communities. For this reason, San Diego County Public Health Services is prioritizing the administration of MenB vaccine to student's currently in this higher risk group.


The South Eastern European Journal of Public Health (SEEJPH) is an open-access worldwide peer-reviewed journal involving all areas of health sciences. SEEJPH welcomes submissions of scientists and researchers from everywhere in the world pertinent notably to Eastern European and different transition nations on the earth. The ACLS training course is designed for medical professionals who've expertise in patient care and covers advanced cardiac care techniques for treating severe trauma or cardiac arrest. Medical professionals must be licensed in CPR and should present proof of certification to be able to enroll in a class. Coaching and certification in Primary Life Support (BLS) while not all the time required, is really useful.
The byline on this bill reads, To provide reasonably priced, high quality health care for all Americans and scale back the expansion in health care spending, and for other purposes." So principally we get extra stuff that's higher than the old stuff and pay less for it? There cannot possibly be a catch. Just one,017.seventy five pages to go. Postmodernism is 'an try to question the basic philosophical and political premises of the West. It argues that most of the ideas we take without any consideration—including fact, morality, and objectivity—are culturally 'constructed" To postmodernists, truth is just what the powerful say is true.
I have heard from many new clinic sufferers who've waited months to see a brand new major care doctor (as soon as they misplaced one insurance coverage plan after which scrambled without end to find one other insurance company that would really cover them). Naturally, these tended to be center-aged individuals who already had gathered a medical problem or two, and thus kept getting denied protection for having these problems, leaving them able the place they may not be taken care of for the medical points they've because of the problems they've. In other words, barring hordes of money that nobody has anymore, the only means you can get real health care protection is in case you are already healthy and do not want it as urgently within the first place. In the meantime, the remainder of us are subjected to lesser or no care, and pretty quickly everyone is petrified of the gerbil that may seal their doom.
Talk about gut-wrenching. First, I did not suppose my presence was that vital to him. Second, I had a tremendous job because the managing editor of a community newspaper. It was my dream job, truly, and I brought home a fantastic earnings for our family. Begin with small, specific goals, like walking briskly 10 minutes a day, three days every week, and construct up slowly from there. Maintain an exercise log to track your progress. You can confer with the sample exercise log. As you turn out to be fitter, slowly increase your tempo, the size of time you are energetic, and how often you might be active.
Swim jets, like the Badu Stream beneath, can be found to be let into pool sides or over-the-wall jets can be utilized. Apart from his job, he has lived a healthy life-style. Before the chemo caught up with him, he was nonetheless driving his bike 5-10 miles a day! He was by no means a smoker or drinker and at all times took care of his body. Actually, our household launched our complete neighborhood and a small segment of San Diego to Rollerblades!


Join today and get exposure to thousands of job seekers on MichianaJobsOnline.com.
Access thousands of job seekers.
With our Job Posting System you can post your job openings in just a few minutes. It's simple, fast and cost-effective.
Post Multiple Jobs in Minutes.
Create and post multiple job opportunities on Michiana Jobs Online - quickly and easily. Save time, lower your recruiting costs and fill your positions with the most qualified candidates.
Job postings are live for 30 days. More live time for your Job Postings means more opportunity for candidates to view your jobs.
Post Jobs Today. Receive Resumes in Seconds.
Job postings appear on Michiana Jobs Online instantaneously after they are submitted. Job seekers can submit resumes in seconds after viewing your job postings.
Post jobs and manage your Account at your convenience, 24/7, using your Account control panel.
Job seekers can view your opportunities by category for a more focused search.
Michiana Jobs Online provides effective ways for you to stay ahead of the competition, improve hiring efficiencies and attract today's top talent.


The Suman 1.8m C Band VSAT Satellite Dish is a versatile satellite dish that can be used for VSAT or receive only (TVRO) applications. Suman 1.8m C Band VSAT Satellite Dish is type approved for VSAT operations with several satellite operators including Apstar, Indosat, Asiasat, Chinasat, and Intelsat.
The Suman 1.8m C Band VSAT Satellite Dish is a versatile satellite dish that can be used for VSAT or receive only (TVRO) applications. Suman 1.8m C Band VSAT Satellite Dish is type approved for VSAT operations with several satellite operators including Apstar, Indosat, Asiasat, Chinasat, and Intelsat. Suman 1.8m C Band VSAT Satellite Dish utilises a one piece single reflector design manufactured using SMC to provide maximum performance and the highest degree of protection from corrosion maximising the life of the antenna. Suman 1.8m C Band VSAT Satellite Dish utilises an azimuth over elevation mount is constructed from galvanised and stainless steel. Suman's single reflector 1.8m C Band VSAT Satellite Dish can be easily assembled and installed by a one man team reducing installation costs and the required manpower.
Suman 1.8m C Band VSAT Satellite Dish is supplied in x-pol feedhorn configuration and includes a transmit reject filter for the Rx port of the feedhorn.
One piece SMC reflector with high surface accuracy.
Excellent sidelobe performance, meeting or exceeding CCIR 580-IV (ITU-R, S.580-5 and S.465-5).
Designed to be installed by a single technician.
Hot dip galvanised steel mount.
Suman are the leading manufacturer of high grade satellite dishes. The company has a strong focus on research and development that has afforded Suman an exceptional reputation. Suman's practical design mantra has significantly reduced assembly time which results in measurable cost saving for the customer during installation.