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PISA: A Non-Volatile Processing-in-Sensor Accelerator for Imaging Systems
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DOI：10.1109/TETC.2023.3292251.png)
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This work proposes a Processing-In-Sensor Accelerator, namely PISA, as a flexible, energy-efficient, and high-performance solution for real-time and smart image processing in AI devices. PISA intrinsically implements a coarse-grained convolution operation in Binarized-Weight Neural Networks (BWNNs) leveraging a novel compute-pixel with non-volatile weight storage at the sensor side. This remarkably reduces the power consumption of data conversion and transmission to an off-chip processor. The design is completed with a bit-wise near-sensor in-memory computing unit to process the remaining network layers. Once the object is detected, PISA switches to typical sensing mode to capture the image for a fine-grained convolution using only a near-sensor processing unit. Our circuit-to-application co-simulation results on a BWNN acceleration demonstrate minor accuracy degradation on various image datasets in coarse-grained evaluation compared to baseline BWNN models, while PISA achieves a frame rate of 1000 and efficiency of similar to 1.74 TOp/s/W. Lastly, PISA substantially reduces data conversion and transmission energy by similar to 84% compared to a baseline.
Keyword:
Magnetic memories
processing-in-sensor
accelerator
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