Presentation
Late Breaking Results: Less Sense Makes More Sense: In-Sensor Compressive Learning for Efficient Machine Vision
DescriptionIntegrating deep learning and image sensors has significantly transformed machine vision applications. Yet, conventional high-resolution image acquisition schemes enabled by imagers are energy-inefficient for deep learning, as they involve excessive data quantization and transmission overhead. To address this challenge, we propose a lightweight in-sensor compressive learning framework that integrates a compressive learning-based encoder within image sensors for task-specific feature extraction. Our framework encodes raw images into adaptive low-dimensional representations using only a 1-bit encoder by joint optimization with downstream machine vision tasks. It achieves 10× data compression, a minimum of 1.6% accuracy loss in the task, and 3.93× energy savings at the sensor-end, outperforming prior arts.
Event Type
Late Breaking Results
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby
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