Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rethinking floating point for deep learning   Reducing hardware overhead of neural networks for faster or lower power
inference and training is an active area of research. Uniform quantization
using integer multiply-add has been thoroughly investigated, which requires
learning many quantization parameters, fine-tuning training or other
prerequisites. Little effort is made to improve floating point relative to this
baseline; it remains energy inefficient, and word size reduction yields drastic
loss in needed dynamic range. We improve floating point to be more energy
efficient than equivalent bit width integer hardware on a 28 nm ASIC process
while retaining accuracy in 8 bits with a novel hybrid log multiply/linear add,
Kulisch accumulation and tapered encodings from Gustafson's posit format. With
no network retraining, and drop-in replacement of all math and float32
parameters via round-to-nearest-even only, this open-sourced 8-bit log float is
within 0.9% top-1 and 0.2% top-5 accuracy of the original float32 ResNet-50 CNN
model on ImageNet. Unlike int8 quantization, it is still a general purpose
floating point arithmetic, interpretable out-of-the-box. Our 8/38-bit log float
multiply-add is synthesized and power profiled at 28 nm at 0.96x the power and
1.12x the area of 8/32-bit integer multiply-add. In 16 bits, our log float
multiply-add is 0.59x the power and 0.68x the area of IEEE 754 float16 fused
multiply-add, maintaining the same signficand precision and dynamic range,
proving useful for training ASICs as well.

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The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg