PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs

TL;DR

PROM introduces a novel quantization method for depthwise-separable CNNs, selectively using ternary and 8-bit weights to significantly reduce energy and storage costs while maintaining accuracy.

Contribution

It proposes a simple, effective quantization scheme that assigns different bit-widths to different parts of modern CNNs, optimizing efficiency.

Findings

Reduces energy cost by 23.9x on MobileNetV2

Decreases storage size by 2.7x compared to float16

Maintains similar classification accuracy on ImageNet

Abstract

Convolutional neural networks (CNNs) are crucial for computer vision tasks on resource-constrained devices. Quantization effectively compresses these models, reducing storage size and energy cost. However, in modern depthwise-separable architectures, the computational cost is distributed unevenly across its components, with pointwise operations being the most expensive. By applying a general quantization scheme to this imbalanced cost distribution, existing quantization approaches fail to fully exploit potential efficiency gains. To this end, we introduce PROM, a straightforward approach for quantizing modern depthwise-separable convolutional networks by selectively using two distinct bit-widths. Specifically, pointwise convolutions are quantized to ternary weights, while the remaining modules use 8-bit weights, which is achieved through a simple quantization-aware training procedure. Additionally, by quantizing activations to 8-bit, our method transforms pointwise convolutions with ternary weights into int8 additions, which enjoy broad support across hardware platforms and effectively eliminates the need for expensive multiplications. Applying PROM to MobileNetV2 reduces the model's energy cost by more than an order of magnitude (23.9x) and its storage size by 2.7x compared to the float16 baseline while retaining similar classification performance on ImageNet. Our method advances the Pareto frontier for energy consumption vs. top-1 accuracy for quantized convolutional models on ImageNet. PROM addresses the challenges of quantizing depthwise-separable convolutional networks to both ternary and 8-bit weights, offering a simple way to reduce energy cost and storage size.