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In this work, we propose to quantize all parts of standard classificationnetworks and replace the activation-weight--multiply step with a simpletable-based lookup. This approach results in networks that are free offloating-point operations and free of multiplications, suitable for direct FPGAand ASIC implementations. It also provides us with two simple measures ofper-layer and network-wide compactness as well as insight into the distributioncharacteristics of activationoutput and weight values. We run controlledstudies across different quantization schemes, both fixed and adaptive and,within the set of adaptive approaches, both parametric and model-free. Weimplement our approach to quantization with minimal, localized changes to thetraining process, allowing us to benefit from advances in trainingcontinuous-valued network architectures. We apply our approach successfully toAlexNet, ResNet, and MobileNet. We show results that are within 1.6% of thereported, non-quantized performance on MobileNet using only 40 entries in ourtable. This performance gap narrows to zero when we allow tables with 320entries. Our results give the best accuracies among multiply-free networks.

Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference