GradFreeBits: Gradient Free Bit Allocation for Dynamic Low Precision Neural Networks

TL;DR

GradFreeBits introduces a novel joint optimization method combining gradient-based and gradient-free techniques to effectively allocate bits in dynamic quantized neural networks, improving performance on standard benchmarks.

Contribution

The paper proposes GradFreeBits, a new approach that optimizes bit allocation in dynamic QNNs using alternating gradient-based and gradient-free methods, addressing discrete optimization challenges.

Findings

Achieves comparable or better performance than state-of-the-art low precision networks.

Effective on CIFAR10/100 and ImageNet classification tasks.

Extensible to other neural network parameter optimization problems.

Abstract

Quantized neural networks (QNNs) are among the main approaches for deploying deep neural networks on low resource edge devices. Training QNNs using different levels of precision throughout the network (dynamic quantization) typically achieves superior trade-offs between performance and computational load. However, optimizing the different precision levels of QNNs can be complicated, as the values of the bit allocations are discrete and difficult to differentiate for. Also, adequately accounting for the dependencies between the bit allocation of different layers is not straight-forward. To meet these challenges, in this work we propose GradFreeBits: a novel joint optimization scheme for training dynamic QNNs, which alternates between gradient-based optimization for the weights, and gradient-free optimization for the bit allocation. Our method achieves better or on par performance with current state of the art low precision neural networks on CIFAR10/100 and ImageNet classification. Furthermore, our approach can be extended to a variety of other applications involving neural networks used in conjunction with parameters which are difficult to optimize for.