Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
Recent work has shown that performing inference with fast, very-low-bitwidth (e.g., 1 to 2 bits) representations of values in models can yield surprisingly accurate results. However, although 2-bit approximated networks have been shown to be quite accurate, 1 bit approximations, which are twice as fast, have restrictively low accuracy. We propose a method to train models whose weights are a mixture of bitwidths, that allows us to more finely tune the accuracy/speed trade-off. We present the “middle-out” criterion for determining the bitwidth for each value, and show how to integrate it into training models with a desired mixture of bitwidths. We evaluate several architectures and binarization techniques on the ImageNet dataset. We show that our heterogeneous bitwidth approximation achieves superlinear scaling of accuracy with bitwidth. Using an average of only 1.4 bits, we are able to outperform state-of-the-art 2-bit architectures.
 This paper presents an extension of binary networks, and the main idea is to use different bit rates for different layers so we can further reduce bitrate of the overall net, and achieve better performance (speed / memory). The paper addresses a real problem which is meaningful, and provides interesting insights, but it is more of an extension. The description of the Heterogeneous Bitwidth Binarization algorithm is interesting and simple, and potentially can be practical, However it also adds more complication to real world implementations, and might not be an elegant enough approach for practical usages. Experiments wise, the paper has done solid experiments comparing with existing approaches and showed the gain. Results are promising. Overall, I am leaning towards a rejection mostly due to limited novelty.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
Recent work has shown that performing inference with fast, very-low-bitwidth (e.g., 1 to 2 bits) representations of values in models can yield surprisingly accurate results. However, although 2-bit approximated networks have been shown to be quite accurate, 1 bit approximations, which are twice as fast, have restrictively low accuracy. We propose a method to train models whose weights are a mixture of bitwidths, that allows us to more finely tune the accuracy/speed trade-off. We present the “middle-out” criterion for determining the bitwidth for each value, and show how to integrate it into training models with a desired mixture of bitwidths. We evaluate several architectures and binarization techniques on the ImageNet dataset. We show that our heterogeneous bitwidth approximation achieves superlinear scaling of accuracy with bitwidth. Using an average of only 1.4 bits, we are able to outperform state-of-the-art 2-bit architectures.
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 presents an extension of binary networks, and the main idea is to use different bit rates for different layers so we can further reduce bitrate of the overall net, and achieve better performance (speed / memory). The paper addresses a real problem which is meaningful, and provides interesting insights, but it is more of an extension. The description of the Heterogeneous Bitwidth Binarization algorithm is interesting and simple, and potentially can be practical, However it also adds more complication to real world implementations, and might not be an elegant enough approach for practical usages. Experiments wise, the paper has done solid experiments comparing with existing approaches and showed the gain. Results are promising. Overall, I am leaning towards a rejection mostly due to limited novelty.
assistant
 This paper presents an extension of binary networks, and the main idea is to use different bit rates for different layers so we can further reduce bitrate of the overall net, and achieve better performance (speed / memory). The paper addresses a real problem which is meaningful, and provides interesting insights, but it is more of an extension. The description of the Heterogeneous Bitwidth Binarization algorithm is interesting and simple, and potentially can be practical, However it also adds more complication to real world implementations, and might not be an elegant enough approach for practical usages. Experiments wise, the paper has done solid experiments comparing with existing approaches and showed the gain. Results are promising. Overall, I am leaning towards a rejection mostly due to limited novelty.