LoRASensE: Learnable Low-Rank Acquisition in Sensors for Efficient Edge Machine Vision

Le résumé fourni par la source

Integrating deep learning with ubiquitous image sensors has empowered various edge vision applications such as classification, segmentation, and detection. Deploying these data-driven applications requires holistic optimizations, from front-end sensing to back-end processing, within the limited resources of edge devices. While significant advances have been made in the efficient processing of sensory data in the back end with optimizations of learning algorithms (e.g., compression) and development of computing hardware (e.g., accelerators), the energy efficiency of front-end sensors remains significantly limited due to conventional high-fidelity image acquisition and the resulting massive off-chip data transfer.This paper proposes a domain-specific visual acquisition method, LoRASensE, learnable low-rank acquisition in sensors tailored for efficient data-driven edge vision applications. LoRASensE is an algorithm-hardware co-design framework that integrates a learned low-rank compressor into image sensors to acquire compressed features. Specifically, this compressor is optimized alongside downstream vision tasks to ensure end-to-end accuracy and is implemented with efficient analog processing hardware. Our extensive evaluations on real-world datasets across various vision applications demonstrate that LoRASensE can achieve a 12.5× compression ratio with a just 1-b compressor, minimal accuracy loss, and 86.9% energy saving compared to the conventional high-fidelity acquisition. Multi-dimensional comparisons further show that LoRASensE also significantly outperforms existing in-sensor compression methods.

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