BitBlade: Energy-Efficient Variable Bit-Precision Hardware Accelerator for Quantized Neural Networks
Sungju Ryu, Hyungjun Kim, Wooseok Yi, Eunhwan Kim, Yulhwa Kim, Taesu Kim, Jae‐Joon Kim · IEEE Journal of Solid-State Circuits · 2022
We introduce an area/energy-efficient precision-scalable neural network accelerator architecture. Previous precision-scalable hardware accelerators have limitations such as the under-utilization of multipliers for low bit-width operations and the large area overhead to support various bit precisions. To mitigate the problems, we first propose a bitwise summation, which reduces the area overhead for the bit-width scaling. In addition, we present a channel-wise aligning scheme (CAS) to efficiently fetch inputs and weights from on-chip SRAM buffers and a channel-first and pixel-last tiling (CFPL) scheme to maximize the utilization of multipliers on various kernel sizes. A test chip was implemented in 28-nm CMOS technology, and the experimental results show that the throughput and energy efficiency of our chip are up to 7.7$\times $and 1.64$\times $higher than those of the state-of-the-art designs, respectively. Moreover, additional 1.5–3.4$\times $throughput gains can be achieved using the CFPL method compared to the CAS.