In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision

TL;DR

This paper presents an in-sensor computing framework for neuromorphic SNNs that significantly reduces data transfer and energy consumption in computer vision tasks, with minimal accuracy loss.

Contribution

It introduces a hardware-software co-design approach that minimizes bandwidth and energy use in SNN-based vision systems, addressing a key bottleneck in current methods.

Findings

Bandwidth reduced by 12-96x

Total energy decreased by 2.32x

Accuracy drops by only 3.8% on ImageNet

Abstract

Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a promising low-power alternative to traditional DNNs for several computer vision (CV) applications. However, most existing SNNs require multiple time steps for acceptable inference accuracy, hindering real-time deployment and increasing spiking activity and, consequently, energy consumption. Recent works proposed direct encoding that directly feeds the analog pixel values in the first layer of the SNN in order to significantly reduce the number of time steps. Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing is efficient using SNNs, the data transfer between the image sensors and the downstream processing costs significant bandwidth and may dominate the total energy. To mitigate this concern, we propose an in-sensor computing hardware-software co-design framework for SNNs targeting image recognition tasks. Our approach reduces the bandwidth between sensing and processing by 12-96x and the resulting total energy by 2.32x compared to traditional CV processing, with a 3.8% reduction in accuracy on ImageNet.