This brief presents a current-mode sensing-computing fusion system for advanced Internet of Things (IoT) machine vision. The key contributions of our work are: (a) a low-voltage of 0.9V multiply-accumulate (MAC) compu...
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This brief presents a current-mode sensing-computing fusion system for advanced Internet of Things (IoT) machine vision. The key contributions of our work are: (a) a low-voltage of 0.9V multiply-accumulate (MAC) computing macro with reconfigurable weights is proposed, enabling efficient on-sensor feature extraction;(b) a weight-flipping method for processing negative signals is employed, reducing both power consumption and circuit complexity;(c) a convolutional horizontal shifting technique with fixed weights is equipped, eliminating power consumption associated with weight updates. A 16 x 16 CCIS prototype, fabricated using a 0.18 mu m CMOS process, achieves a power efficiency of 27.7pJ/frame-pixel at 2000fps. Experimental evaluations in edge feature extraction demonstrate a 32% reduction in power consumption, highlighting the efficiency gains of our approach.
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