Ternary Weight Decomposition and Binary Activation Encoding for Fast and Compact Neural Network
This paper aims to reduce test-time computational load of a deep neural network. Unlike previous methods which factorize a weight matrix into multiple real-valued matrices, our method factorizes both weights and activations into integer and noninteger components. In our method, the real-valued weight matrix is approximated by a multiplication of a ternary matrix and a real-valued co-efficient matrix. Since the ternary matrix consists of three integer values, {-1, 0, +1}, it only consumes 2 bits per element. At test-time, an activation vector that passed from a previous layer is also transformed into a weighted sum of binary vectors, {-1, +1}, which enables fast feed-forward propagation based on simple logical operations: AND, XOR, and bit count. This makes it easier to deploy a deep network on low-power CPUs or to design specialized hardware. In our experiments, we tested our method on three different networks: a CNN for handwritten digits, VGG-16 model for ImageNet classification, and VGG-Face for large-scale face recognition. In particular, when we applied our method to three fully connected layers in the VGG-16, 15x acceleration and memory compression up to 5.2% were achieved with only a 1.43% increase in the top-5 error. Our experiments also revealed that compressing convolutional layers can accelerate inference of the entire network in exchange of slight increase in error.
This paper addresses to reduce test-time computational load of DNNs. Another factorization approach is proposed and shows good results. The comparison to the other methods is not comprehensive, the paper provides good insights.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Ternary Weight Decomposition and Binary Activation Encoding for Fast and Compact Neural Network
This paper aims to reduce test-time computational load of a deep neural network. Unlike previous methods which factorize a weight matrix into multiple real-valued matrices, our method factorizes both weights and activations into integer and noninteger components. In our method, the real-valued weight matrix is approximated by a multiplication of a ternary matrix and a real-valued co-efficient matrix. Since the ternary matrix consists of three integer values, {-1, 0, +1}, it only consumes 2 bits per element. At test-time, an activation vector that passed from a previous layer is also transformed into a weighted sum of binary vectors, {-1, +1}, which enables fast feed-forward propagation based on simple logical operations: AND, XOR, and bit count. This makes it easier to deploy a deep network on low-power CPUs or to design specialized hardware. In our experiments, we tested our method on three different networks: a CNN for handwritten digits, VGG-16 model for ImageNet classification, and VGG-Face for large-scale face recognition. In particular, when we applied our method to three fully connected layers in the VGG-16, 15x acceleration and memory compression up to 5.2% were achieved with only a 1.43% increase in the top-5 error. Our experiments also revealed that compressing convolutional layers can accelerate inference of the entire network in exchange of slight increase in error.
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 addresses to reduce test-time computational load of DNNs. Another factorization approach is proposed and shows good results. The comparison to the other methods is not comprehensive, the paper provides good insights.
assistant
This paper addresses to reduce test-time computational load of DNNs. Another factorization approach is proposed and shows good results. The comparison to the other methods is not comprehensive, the paper provides good insights.