Morsali, Mehrdad, authorNajafi, Deniz, authorShafiee, Amin, authorTabrizchi, Sepehr, authorMercati, Pietro, authorImani, Mohsen, authorRoohi, Arman, authorKhoshavi, Navid, authorNikdast, Mahdi, authorAngizi, Shaahin, authorACM, publisher2025-12-222025-12-222025-06-30Mehrdad Morsali, Deniz Najafi, Amin Shafiee, Sepehr Tabrizchi, Pietro Mercati, Mohsen Imani, Arman Roohi, Navid Khoshavi, Mahdi Nikdast, and Shaahin Angizi. 2025. Event-Driven Spatiotemporal Processing-In- Sensor with Phase Change Memory-based Optical Acceleration. In Great Lakes Symposium on VLSI 2025 (GLSVLSI '25), June 30–July 02, 2025, New Orleans, LA, USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3716368.3735243https://hdl.handle.net/10217/242557This work introduces a novel hybrid electronic-optical processing-in-sensor architecture designed for low-cost, real-time frame processing at the edge. The proposed system enables event detection and integrates a TinyLSTM-based temporal inference model to analyze multiple frames in real time, extracting meaningful spatiotemporal features that trigger an address actuator for region-of-interest selection. By selectively reading out only relevant pixel regions, the architecture significantly reduces data transfer overhead and power consumption. Additionally, it harnesses the efficiency of silicon photonic (SiPh) devices to enable adaptive frame compression techniques and perform convolution operations through intrinsic, conversion-free multiply-accumulate computations. Device-to-architecture simulation results demonstrate 11.2x improvement in performance compared to the state-of-the-art SiPh accelerator achieving 37 KFPS/W. This marks a significant advancement in processing-in-sensor technology, enhancing both computational efficiency and energy savings for edge AI applications.born digitalarticlesenghttps://creativecommons.org/licenses/by/4.0vision sensorsdeep neural networksprocessing-in-sensorsilicon photonicsTextThis work is licensed under a Creative Commons Attribution 4.0 International License.https://doi.org/10.1145/3716368.3735243