An autoencoder with entropy loss, 4-bit quantization, and Huffman coding compresses in-sensor image features by up to 11985x, cutting estimated system energy by 2 to 4.5x with near-baseline accuracy on VWW, hand tracking, and eye tracking.
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OASIS: Optimized Lightweight Autoencoder System for Distributed In-Sensor computing
An autoencoder with entropy loss, 4-bit quantization, and Huffman coding compresses in-sensor image features by up to 11985x, cutting estimated system energy by 2 to 4.5x with near-baseline accuracy on VWW, hand tracking, and eye tracking.