SySMOL: Co-designing Algorithms and Hardware for Neural Networks with Heterogeneous Precisions

• URL: http://arxiv.org/abs/2311.14114v2
• Date: Mon, 6 May 2024 19:32:50 GMT
• Title: SySMOL: Co-designing Algorithms and Hardware for Neural Networks with Heterogeneous Precisions
• Authors: Cyrus Zhou, Pedro Savarese, Vaughn Richard, Zack Hassman, Xin Yuan, Michael Maire, Michael DiBrino, Yanjing Li,
• Abstract summary: Recent quantization techniques have enabled heterogeneous precisions at very fine granularity. These networks require additional hardware to decode the precision settings for individual variables, align the variables, and provide fine-grained mixed-precision compute capabilities. We present an end-to-end co-design approach to efficiently execute networks with fine-grained heterogeneous precisions.
• Score: 20.241671088121144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent quantization techniques have enabled heterogeneous precisions at very fine granularity, e.g., each parameter/activation can take on a different precision, resulting in compact neural networks without sacrificing accuracy. However, there is a lack of efficient architectural support for such networks, which require additional hardware to decode the precision settings for individual variables, align the variables, and provide fine-grained mixed-precision compute capabilities. The complexity of these operations introduces high overheads. Thus, the improvements in inference latency/energy of these networks are not commensurate with the compression ratio, and may be inferior to larger quantized networks with uniform precisions. We present an end-to-end co-design approach encompassing computer architecture, training algorithm, and inference optimization to efficiently execute networks with fine-grained heterogeneous precisions. The key to our approach is a novel training algorithm designed to accommodate hardware constraints and inference operation requirements, outputting networks with input-channel-wise heterogeneous precisions and at most three precision levels. Combined with inference optimization techniques, existing architectures with low-cost enhancements can support such networks efficiently, yielding optimized tradeoffs between accuracy, compression ratio and inference latency/energy. We demonstrate the efficacy of our approach across CPU and GPU architectures. For various representative neural networks, our approach achieves >10x improvements in both compression ratio and inference latency, with negligible degradation in accuracy compared to full-precision networks.

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