Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extended Bit-Plane Compression for Convolutional Neural Network
  Accelerators   After the tremendous success of convolutional neural networks in image
classification, object detection, speech recognition, etc., there is now rising
demand for deployment of these compute-intensive ML models on tightly power
constrained embedded and mobile systems at low cost as well as for pushing the
throughput in data centers. This has triggered a wave of research towards
specialized hardware accelerators. Their performance is often constrained by
I/O bandwidth and the energy consumption is dominated by I/O transfers to
off-chip memory. We introduce and evaluate a novel, hardware-friendly
compression scheme for the feature maps present within convolutional neural
networks. We show that an average compression ratio of 4.4x relative to
uncompressed data and a gain of 60% over existing method can be achieved for
ResNet-34 with a compression block requiring <300 bit of sequential cells and
minimal combinational logic.

36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph