MST-compression: Compressing and Accelerating Binary Neural Networks with Minimum Spanning Tree

TL;DR

This paper introduces MST-compression, a novel method for compressing and accelerating binary neural networks by reordering output channels based on a minimum spanning tree to reduce computation and latency.

Contribution

It proposes a new MST-based architecture and learning algorithm for efficient BNN compression and acceleration, improving performance on edge devices.

Findings

Achieves significant compression with minimal accuracy loss.

Reduces computational cost and latency in BNNs.

Effective on benchmark models.

Abstract

Binary neural networks (BNNs) have been widely adopted to reduce the computational cost and memory storage on edge-computing devices by using one-bit representation for activations and weights. However, as neural networks become wider/deeper to improve accuracy and meet practical requirements, the computational burden remains a significant challenge even on the binary version. To address these issues, this paper proposes a novel method called Minimum Spanning Tree (MST) compression that learns to compress and accelerate BNNs. The proposed architecture leverages an observation from previous works that an output channel in a binary convolution can be computed using another output channel and XNOR operations with weights that differ from the weights of the reused channel. We first construct a fully connected graph with vertices corresponding to output channels, where the distance between two vertices is the number of different values between the weight sets used for these outputs. Then, the MST of the graph with the minimum depth is proposed to reorder output calculations, aiming to reduce computational cost and latency. Moreover, we propose a new learning algorithm to reduce the total MST distance during training. Experimental results on benchmark models demonstrate that our method achieves significant compression ratios with negligible accuracy drops, making it a promising approach for resource-constrained edge-computing devices.