DNN Feature Map Compression using Learned Representation over GF(2)
In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Unlike previous works, the proposed method is based on converting fixed-point activations into vectors over the smallest GF(2) finite field followed by nonlinear dimensionality reduction (NDR) layers embedded into a DNN. Such an end-to-end learned representation finds more compact feature maps by exploiting quantization redundancies within the fixed-point activations along the channel or spatial dimensions. We apply the proposed network architecture to the tasks of ImageNet classification and PASCAL VOC object detection. Compared to prior approaches, the conducted experiments show a factor of 2 decrease in memory requirements with minor degradation in accuracy while adding only bitwise computations.
 In order to compress DNN intermediate feature maps the authors covert fixed-point activations into vectors over the smallest finite field, the Galois field of two elements (GF(2)) and use nonlinear dimentionality reduction layers. The paper reads well and the methods and experiments are generally described in sufficient detail. My main concern with this paper and approach is the performance achieved. According to Table 1 and Table 2 there is a small accuracy benefit from using the proposed approach over the "quantized" SqueezeNet baseline. If I am weighing in the need to alter the network for the proposed approach in comparison with the "quantized" setting then, from practical point of view, I would prefer the later "quantized" approach.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
DNN Feature Map Compression using Learned Representation over GF(2)
In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Unlike previous works, the proposed method is based on converting fixed-point activations into vectors over the smallest GF(2) finite field followed by nonlinear dimensionality reduction (NDR) layers embedded into a DNN. Such an end-to-end learned representation finds more compact feature maps by exploiting quantization redundancies within the fixed-point activations along the channel or spatial dimensions. We apply the proposed network architecture to the tasks of ImageNet classification and PASCAL VOC object detection. Compared to prior approaches, the conducted experiments show a factor of 2 decrease in memory requirements with minor degradation in accuracy while adding only bitwise computations.
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>



 In order to compress DNN intermediate feature maps the authors covert fixed-point activations into vectors over the smallest finite field, the Galois field of two elements (GF(2)) and use nonlinear dimentionality reduction layers. The paper reads well and the methods and experiments are generally described in sufficient detail. My main concern with this paper and approach is the performance achieved. According to Table 1 and Table 2 there is a small accuracy benefit from using the proposed approach over the "quantized" SqueezeNet baseline. If I am weighing in the need to alter the network for the proposed approach in comparison with the "quantized" setting then, from practical point of view, I would prefer the later "quantized" approach.
assistant
 In order to compress DNN intermediate feature maps the authors covert fixed-point activations into vectors over the smallest finite field, the Galois field of two elements (GF(2)) and use nonlinear dimentionality reduction layers. The paper reads well and the methods and experiments are generally described in sufficient detail. My main concern with this paper and approach is the performance achieved. According to Table 1 and Table 2 there is a small accuracy benefit from using the proposed approach over the "quantized" SqueezeNet baseline. If I am weighing in the need to alter the network for the proposed approach in comparison with the "quantized" setting then, from practical point of view, I would prefer the later "quantized" approach.