Abstract

Low-precision quantized neural networks (QNNs) reduce the required memory space, bandwidth, and computational power, and hence are suitable for deployment in applications such as IoT edge devices. Mixed-precision QNNs, where weights commonly have lower precision than activations or different precision is used for different layers, can limit the accuracy loss caused by low-bit quantization, while still benefiting from reduced memory footprint and faster execution. Previous multiple-precision functional units supporting 8-bit, 4-bit, and 2-bit SIMD instructions have limitations, such as large area overhead, under-utilization of multipliers, and wasted memory space for low and mixed bit-width operations. This article introduces BISDU, a bit-serial dot-product unit to support and accelerate execution of mixed-precision low-bit QNNs on resource-constrained microcontrollers. BISDU is a multiplier-less dot-product unit, with frugal hardware requirements (a population count unit and 2:1 multiplexers). The proposed bit-serial dot-product unit leverages the conventional logical operations of a microcontroller to perform multiplications, which enables efficient software implementations of binary ( Xnor ), ternary ( Xor ), and mixed-precision [W×A] ( And ) dot-product operations. The experimental results show that BISDU achieves competitive performance compared to two state-of-the-art units, XpulpNN and Dustin, when executing low-bit-width CNNs. We demonstrate the advantage that bit-serial execution provides by enabling trading accuracy against weight footprint and execution time. BISDU increases the area of the ALU by 68% and the ALU power consumption by 42% compared to a baseline 32-bit RISC-V (RV32IC) microcontroller core. In comparison, XpulpNN and Dustin increase the area by 6.9× and 11.1× and the power consumption by 3.8× and 5.97×, respectively. The bit-serial state-of-the-art, based on a conventional popcount instruction, increases the area by 42% and power by 32%, with BISDU providing a 37% speedup over it.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call