Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: This paper studies an important question: how to reduce memory bandwidth requirement in neural network computation and hence reduce the energy footprint. It proposes to use lossy transform coding before sending network output to memory. My concern with the paper is two-fold:
1) The major technique of transform-domain coding is borrowed from previous work (e.g., Goyal 2001), hence the novelty of the proposed method is in doubt.
2) The implementation details are not clear. For example, I don't know whether the implementation in section 3.1 is based on CPU or FPGA, and how easily Section 3.1 will be implemented on ASIC. For the experimental results are reported in Section 4, we do not know how much memory and how much cache is used. Will the computation of PCA require a lot of on-device memory? 

More detailed comments:
Section 1, 2nd paragraph: GPUs are event more popular than FPGAs and ASICs. Can the proposed method be useful for GPU inference?
Section 1, 3nd paragraph:  The last sentence says "high interdependence between the feature maps and spatial locations of the compute activations". However, it is not clear to me how the proposed method takes spatial location into account.
Section 2: better to review previous work In lossy transform coding
Figure 1: It seems to me Figure 1 is obvious. What is the novelty?
Section 4: better to report the details of computing units and memory size. 
review_title: Official Blind Review #4

final_score: rating: 8: Accept
review_content: The submission proposes to reduce the memory bandwidth (and energy consumption) in CNNs by applying PCA transforms on feature vectors at all spatial locations followed by uniform quantization and variable-length coding.

I appreciate the writing quality: as an outsider to the field of low-power/low-precision deep learning, I found the write-up straightforward and easy to follow. It’s harder for me to precisely assess the significance of the proposed approach, but at a high level it looks reasonable and is backed by convincing empirical evidence.

Small comment: I don’t believe the submission is following the ICLR 2020 format strictly: the font looks different, and the margins look tighter.
review_title: Official Blind Review #3

final_score: rating: 3: Weak Reject
review_content: A lossy transform coding approach was proposed to reduce the memory bandwidth of edge devices deploying CNNs. For this purpose, the proposed method compresses highly correlated feature maps using variable length coding. In the experimental analyses, the proposed method outperforms some of the previous work in terms of the compression ratio and accuracy for training ResNet-34 and MobileNetV2. 

The proposed method and initial results are promising. However, the paper and the work should be improved for a clear acceptance:

- Some parts of the method need to be explained more clearly:

– In the statement “Due to the choice of 1 × 1 × C blocks, the PCA transform essentially becomes a 1 × 1 tensor convolution kernel”, what do you mean by “the PCA transform becomes a convolution kernel.”?

- Could you please further explain how you compute PCA using batch data, how you update online and how you employ that in convolution weights together with BN? Please also explain the following in detail:

(I) “To avoid this, we calculate the covariance matrix layer by layer, gradually applying the quantization.” What is the quantization method you applied, and how did you apply it gradually?

(II) “The PCA matrix is calculated after quantization of the weights is performed, and is itself quantized to 8 bits.” How did you quantize the weights, how did you calculate PCA using quantized weights and how did you quantize them to 8 bits?

- Could you please explain the following settings, more precisely: direct quantization of the activations; quantization of PCA coefficients; direct quantization followed by VLC; and full encoder chain comprising PCA, quantization, and VLC? Please note that there are various methods and algorithms which can be used for these quantization steps. Therefore, please explain your proposed or employed quantization methods more clearly and precisely.

–  Please clarify the statement “This projection helps to concentrate most of the data in part of the channels, and then have the ability to write less data that layers.”.

- Did you apply your methods to larger networks such as larger ResNets, VGG like architectures, Inception etc?

- I also suggest you to perform experiments on different smaller and larger datasets, such as Cifar 10/100, face recognition datasets etc., to examine generalization of the proposed methods at least among different datasets.
review_title: Official Blind Review #2

Meta-review: The paper proposed the use of a lossy transform coding approach to to reduce the memory bandwidth brought by the storage of intermediate activations. It has shown the proposed method can bring good memory usage while maintaining the the accuracy.
The main concern on this paper is the limited novelty. The lossy transform coding is borrowed from other domains and only the use of it on CNN intermediate activation is new, which seems insufficient. 
Decision: Reject