BitX: Empower Versatile Inference with Hardware Runtime Pruning

Abstract

Classic DNN pruning mostly leverages software-based methodologies to tackle the accuracy/speed tradeoff, which involves complicated procedures like critical parameter searching, fine-tuning and sparse training to find the best plan. In this paper, we explore the opportunities of hardware runtime pruning and propose a hardware runtime pruning methodology, termed as “BitX” to empower versatile DNN inference. It targets the abundant useless bits in the parameters, pinpoints and prunes these bits on-the-fly in the proposed BitX accelerator. The versatility of BitX lies in: (1) software effortless; (2) orthogonal to the software-based pruning; and (3) multi-precision support (including both floating point and fixed point). Empirical studies on image classification and object detection models highlight the following results: (1) up to 4.82x speedup over the original non-pruned DNN and 14.76x speedup collaborated with the software-pruned DNN; (2) up to 0.07% and 0.9% higher accuracy for the floating-point and fixed-point DNN, respectively; (3) 2.00x and 3.79x performance improvement over the state-of-the-art accelerators, with 0.039 mm2 and 68.62 mW (floating-point 32), 36.41 mW(16-bit fixed point) power consumption under TSMC 28 nm technology library.