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Keyword: mixed precision ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   HAQ: Hardware-Aware Automated Quantization With Mixed Precision

   Wang, Liu, Lin, Lin and Han · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2019

   The paper introduces HAQ, a reinforcement-learning framework that uses hardware simulator feedback to choose per-layer quantization bitwidths for neural networks on different hardware.

   Unchecked2 claims
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   1. UncheckedHAQ's automated mixed-precision quantization cut latency by 1.4-1.95x and energy by 1.9x versus fixed 8-bit, with negligible accuracy loss.“Our framework effectively reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with negligible loss of accuracy compared with the fixed bitwidth (8 bits) quantization.”
   2. UncheckedThe HAQ framework finds that the best quantization policies differ greatly between edge and cloud hardware and under latency, energy or model-size limits.“Our framework reveals that the optimal policies on different hardware architectures (i.e., edge and cloud architectures) under different resource constraints (i.e., latency, energy and model size) are drastically different.”