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Recent quantization techniques have enabled heterogeneous precisions at veryfine granularity, e.g., each parameter/activation can take on a differentprecision, 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 forindividual variables, align the variables, and provide fine-grainedmixed-precision compute capabilities. The complexity of these operationsintroduces high overheads. Thus, the improvements in inference latency/energyof these networks are not commensurate with the compression ratio, and may beinferior to larger quantized networks with uniform precisions.  We present an end-to-end co-design approach encompassing computerarchitecture, training algorithm, and inference optimization to efficientlyexecute networks with fine-grained heterogeneous precisions. The key to ourapproach is a novel training algorithm designed to accommodate hardwareconstraints and inference operation requirements, outputting networks withinput-channel-wise heterogeneous precisions and at most three precision levels.Combined with inference optimization techniques, existing architectures withlow-cost enhancements can support such networks efficiently, yielding optimizedtradeoffs 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 >10ximprovements in both compression ratio and inference latency, with negligibledegradation in accuracy compared to full-precision networks.

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