MINT: Dynamic-Precision CNN Inference with MSDF Digit-Serial Arithmetic on FPGA

We present MINT, a dynamic-precision CNN inference accelerator based on left-to-right (LR) arithmetic.

LR arithmetic computes in most-significant-digit-first manner and exposes useful partial results early so that the computation can be terminated once the desired precision is achieved.

At the core, there is a MSDF serial-parallel inner-product unit, which uses redundant signed-digit representation to compute each convolution window.

A budget-constrained greedy search profiles all convolution layers from INT2 to INT7 and selects the lowest precision per layer while constraining total accuracy loss to within 2\% of the INT8 baseline for VGG-16 and ResNet-18 networks.

The design is synthesized on a Xilinx Zynq-7020 at \SI{200}{\mega\hertz}, and uses 5.64 average bits for VGG-16 and 6.04 for ResNet-18, while achieving 19.86 GOPS and 29.51 GOPS/W on VGG-16, and 18.86 GOPS and 26.40 GOPS/W on ResNet-18.

This corresponds to 32.6\% and 26.0\% higher throughput and 82.10\% and 62.90\% higher energy efficiency than INT8 with only 1.81\% and 1.96\% drops relative to the INT8 baseline.

Compared with representative prior FPGA CNN accelerators considered in this study, MINT delivers the highest energy efficiency among the listed VGG-16 and ResNet-18 designs on Zynq-7020 platform.