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BEGIN:VEVENT
DTSTAMP:20260402T024533Z
LOCATION:Level 2 Lobby
DTSTART;TZID=America/Los_Angeles:20250623T180000
DTEND;TZID=America/Los_Angeles:20250623T190000
UID:dac_DAC 2025_sess287_LBR119@linklings.com
SUMMARY:Late Breaking Results: Less Sense Makes More Sense: In-Sensor Comp
 ressive Learning for Efficient Machine Vision
DESCRIPTION:Yiwen Liang and Weidong Cao (George Washington University)\n\n
 Integrating deep learning and image sensors has significantly transformed 
 machine vision applications. Yet, conventional high-resolution image acqui
 sition schemes enabled by imagers are energy-inefficient for deep learning
 , as they involve excessive data quantization and transmission overhead. T
 o address this challenge, we propose a lightweight in-sensor compressive l
 earning framework that integrates a compressive learning-based encoder wit
 hin image sensors for task-specific feature extraction. Our framework enco
 des 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 t
 ask, and 3.93× energy savings at the sensor-end, outperforming prior arts.
 \n\n
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