Trained Ternary Quantization
Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with limited power budgets. To solve this problem, we propose Trained Ternary Quantization (TTQ), a method that can reduce the precision of weights in neural networks to ternary values. This method has very little accuracy degradation and can even improve the accuracy of some models (32, 44, 56-layer ResNet) on CIFAR-10 and AlexNet on ImageNet. And our AlexNet model is trained from scratch, which means it’s as easy as to train normal full precision model. We highlight our trained quantization method that can learn both ternary values and ternary assignment. During inference, only ternary values (2-bit weights) and scaling factors are needed, therefore our models are nearly 16× smaller than full- precision models. Our ternary models can also be viewed as sparse binary weight networks, which can potentially be accelerated with custom circuit. Experiments on CIFAR-10 show that the ternary models obtained by trained quantization method outperform full-precision models of ResNet-32,44,56 by 0.04%, 0.16%, 0.36%, respectively. On ImageNet, our model outperforms full-precision AlexNet model by 0.3% of Top-1 accuracy and outperforms previous ternary models by 3%.
This paper studies in depth the idea of quantizing down convolutional layers to 3 bits, with a different positive and negative per-layer scale. It goes on to provide an exhaustive analysis of performance (essentially no loss) on real benchmarks (this paper is remarkably MNIST-free). The relevance of this paper is that it likely provides a lower bound on quantization approaches that don*t sacrifice any performance, and hence can plausibly become the approach of choice for resource-constrained inference, and might suggest new hardware designs to take advantage of the proposed structure. Furthermore, the paper provides power measurements, which is really the main metric that anyone working seriously in that space cares about. (Nit: I don*t see measurements for the full-precision baseline). I would have loved to see a SOTA result on ImageNet and a result on a strong LSTM baseline to be fully convinced. I would have also liked to see discussion of the wall time to result using this training procedure.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Trained Ternary Quantization
Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with limited power budgets. To solve this problem, we propose Trained Ternary Quantization (TTQ), a method that can reduce the precision of weights in neural networks to ternary values. This method has very little accuracy degradation and can even improve the accuracy of some models (32, 44, 56-layer ResNet) on CIFAR-10 and AlexNet on ImageNet. And our AlexNet model is trained from scratch, which means it’s as easy as to train normal full precision model. We highlight our trained quantization method that can learn both ternary values and ternary assignment. During inference, only ternary values (2-bit weights) and scaling factors are needed, therefore our models are nearly 16× smaller than full- precision models. Our ternary models can also be viewed as sparse binary weight networks, which can potentially be accelerated with custom circuit. Experiments on CIFAR-10 show that the ternary models obtained by trained quantization method outperform full-precision models of ResNet-32,44,56 by 0.04%, 0.16%, 0.36%, respectively. On ImageNet, our model outperforms full-precision AlexNet model by 0.3% of Top-1 accuracy and outperforms previous ternary models by 3%.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper studies in depth the idea of quantizing down convolutional layers to 3 bits, with a different positive and negative per-layer scale. It goes on to provide an exhaustive analysis of performance (essentially no loss) on real benchmarks (this paper is remarkably MNIST-free). The relevance of this paper is that it likely provides a lower bound on quantization approaches that don*t sacrifice any performance, and hence can plausibly become the approach of choice for resource-constrained inference, and might suggest new hardware designs to take advantage of the proposed structure. Furthermore, the paper provides power measurements, which is really the main metric that anyone working seriously in that space cares about. (Nit: I don*t see measurements for the full-precision baseline). I would have loved to see a SOTA result on ImageNet and a result on a strong LSTM baseline to be fully convinced. I would have also liked to see discussion of the wall time to result using this training procedure.
assistant
This paper studies in depth the idea of quantizing down convolutional layers to 3 bits, with a different positive and negative per-layer scale. It goes on to provide an exhaustive analysis of performance (essentially no loss) on real benchmarks (this paper is remarkably MNIST-free). The relevance of this paper is that it likely provides a lower bound on quantization approaches that don*t sacrifice any performance, and hence can plausibly become the approach of choice for resource-constrained inference, and might suggest new hardware designs to take advantage of the proposed structure. Furthermore, the paper provides power measurements, which is really the main metric that anyone working seriously in that space cares about. (Nit: I don*t see measurements for the full-precision baseline). I would have loved to see a SOTA result on ImageNet and a result on a strong LSTM baseline to be fully convinced. I would have also liked to see discussion of the wall time to result using this training procedure.