MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization

TL;DR

MVQ introduces a novel vector quantization method with pruning and masked k-means to improve DNN compression and acceleration, achieving higher accuracy, energy efficiency, and reduced hardware size.

Contribution

The paper proposes MVQ, a new approach combining pruning and masked k-means for better weight approximation and efficient hardware implementation for DNN acceleration.

Findings

Outperforms conventional VQ in accuracy at similar compression ratios.

Reduces FLOPs and enhances energy efficiency in DNN inference.

Achieves 2.3× energy efficiency boost and 55% smaller systolic array in ASIC.

Abstract

Vector quantization(VQ) is a hardware-friendly DNN compression method that can reduce the storage cost and weight-loading datawidth of hardware accelerators. However, conventional VQ techniques lead to significant accuracy loss because the important weights are not well preserved. To tackle this problem, a novel approach called MVQ is proposed, which aims at better approximating important weights with a limited number of codewords. At the algorithm level, our approach removes the less important weights through N:M pruning and then minimizes the vector clustering error between the remaining weights and codewords by the masked k-means algorithm. Only distances between the unpruned weights and the codewords are computed, which are then used to update the codewords. At the architecture level, our accelerator implements vector quantization on an EWS (Enhanced weight stationary) CNN accelerator and proposes a sparse systolic array design to maximize the benefits brought by masked vector quantization.\\ Our algorithm is validated on various models for image classification, object detection, and segmentation tasks. Experimental results demonstrate that MVQ not only outperforms conventional vector quantization methods at comparable compression ratios but also reduces FLOPs. Under ASIC evaluation, our MVQ accelerator boosts energy efficiency by 2.3$\times$ and reduces the size of the systolic array by 55\% when compared with the base EWS accelerator. Compared to the previous sparse accelerators, MVQ achieves 1.73$\times$ higher energy efficiency.