Late Breaking Results: Less Sense Makes More Sense: In-Sensor Compressive Learning for Efficient Machine Vision

Integrating deep learning and image sensors has significantly transformed machine vision applications. Yet, conventional highresolution image acquisition schemes enabled by imagers are energyinefficient for deep learning, as they involve excessive data quantization and transmission overhead. To addr...

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Bibliographic Details
Published in:2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 2
Main Authors: Liang, Yiwen, Cao, Weidong
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2025
Subjects:
Data compression
Deep learning
Design automation
Energy conservation
Feature extraction
Image coding
Image sensors
Machine vision
Optimization
Quantization (signal)
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Summary:Integrating deep learning and image sensors has significantly transformed machine vision applications. Yet, conventional highresolution image acquisition schemes enabled by imagers are energyinefficient 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 taskspecific 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 \times data compression, a minimum of 1.6% accuracy loss in the task, and 3.93 \times energy savings at the sensor-end, outperforming prior arts.
DOI:10.1109/DAC63849.2025.11132931