Enhancing the Energy Efficiency and Robustness of tinyML Computer Vision Using Coarsely-Quantized Log-Gradient Input Images

Abstract

This paper studies the merits of applying log-gradient input images to convolutional neural networks (CNNs) for tinyML computer vision (CV). We show that log gradients enable: (i) aggressive 1-bit quantization of first-layer inputs, (ii) potential CNN resource reductions, (iii) inherent insensitivity to illumination changes (1.7% accuracy loss across 2 − 5 ⋅⋅⋅2 3 brightness variation vs. up to 10% for JPEG), and (iv) robustness to adversarial attacks (>10% higher accuracy than JPEG-trained models). We establish these results using the PASCAL RAW image data set and through a combination of experiments using quantization threshold search, neural architecture search, and a fixed three-layer network. The latter reveal that training on log-gradient images leads to higher filter similarity, making the CNN more prunable. The combined benefits of aggressive first-layer quantization, CNN resource reductions, and operation without tight exposure control and image signal processing (ISP) are helpful for pushing tinyML CV toward its ultimate efficiency limits.