PISA: A Non-Volatile Processing-in-Sensor Accelerator for Imaging Systems

Shaahin Angizi, Sepehr Tabrizchi, David Z. Pan, Arman Roohi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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 $\sim$∼ 1.74 TOp/s/W. Lastly, PISA substantially reduces data conversion and transmission energy by $\sim$∼ 84% compared to a baseline.

Original languageEnglish (US)
Pages (from-to)962-972
Number of pages11
JournalIEEE Transactions on Emerging Topics in Computing
Issue number4
StatePublished - Oct 1 2023

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications


  • Magnetic memories
  • accelerator
  • processing-in-sensor


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