Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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 architectures derived from modified SqueezeNet and MobileNetV2
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.

29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn