WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic
Renkun Ni, H.M. Chu, Oscar Castañeda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein · Repository for Publications and Research Data (ETH Zurich) · 2021
Low-precision neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity. Nonetheless, these products are accumulated using high-precision (typically 32-bit) additions, an operation that dominates the arithmetic complexity of inference when using extreme quantization (e.g., binary weights). To further optimize inference, we propose WrapNet that adapts neural networks to use low-precision (8-bit) additions in the accumulators, achieving classification accuracy comparable to their 32-bit counterparts. We achieve resilience to low-precision accumulation by inserting a cyclic activation layer, as well as an overflow penalty regularizer. We demonstrate the efficacy of our approach on both software and hardware platforms.