Optimization of General Matrix Multiply Library for Ternary Weight for Fast DNN Inference

Abstract

Efficient implementation of deep neural networks (DNNs) on CPU-based systems is critical owing to the proliferation of applications in embedded and Internet of Things systems. Nowdays, most CPUs are equipped with single instruction multiple data (SIMD) instructions, which are used to implement an efficient general matrix multiply (GEMM) library for accelerating DNN inference. Quantized neural networks are actively investigated to simplify DNN computation and memory requirements; however, the current CPU libraries do not efficiently support arithmetic operations below eight bits. Hence, we developed TernGEMM, a GEMM library composed of SIMD instructions for DNNs with ternary weights and sub-8-bit activations. TernGEMM is implemented using simple logical operations that replace the long-latency multiply-add operation. Instead of fixing the accumulation bit precision as 32-bit, TernGEMM accumulates the partial sums in a bit-incremental manner to exploit parallelism in 8-bit and 16-bit SIMD instructions. Furthermore, we propose different tile sizes for TernGEMM to better support the diverse dimensions of DNNs. Compared with a state-of-the-art reduced precision DNN GEMM library, i.e., GEMMLowp, TernGEMM achieve x1.785 to x4.147 speedup for ResNet50, MobileNet-V2, and EfficientNet-B0, as evaluated on both Intel and ARM CPUs.N