HOGEye: Neural Approximation of HOG Feature Extraction in RRAM-Based 3D-Stacked Image Sensors

Abstract

Many computer vision tasks, ranging from recognition to multi-view registration, operate on feature representation of images rather than raw pixel intensities. However, conventional pipelines for obtaining these representations incur significant energy consumption due to pixel-wise analog-to-digital (A/D) conversions and costly storage and computations. In this paper, we propose HOGEye, an efficient near-pixel implementation for a widely-used feature extraction algorithm—Histograms of Oriented Gradients (HOG). HOGEye moves the key but computation-intensive derivative extraction (DE) and histogram generation (HG) steps into the analog domain by applying a novel neural approximation method in a resistive random-access memory (RRAM)-based 3D-stacked image sensor. The co-location of perception (sensor) and computation (DE and HG) and the alleviation of A/D conversions allow HOGEye design to achieve significant energy saving. With negligible detection rate degradation, the entire HOGEye sensor system consumes less than 48μW@30fps for an image resolution of 256 × 256 (equivalent to 24.3pJ/pixel) while the processing part only consumes 14.1pJ/pixel, achieving more than 2.5 × energy efficiency improvement than the state-of-the-art designs.