@inproceedings{b9d4c5021fd34b6fbc8709b6ed61a9a8,
title = "Energy-Efficient Near-Sensor Event Detector Based on Multilevel Ga2O3 RRAM",
abstract = "In this paper, a cost-effective Near-Sensor Processing (NSP) platform is developed based on an experimentally-measured TiTiN/Ga2O3/Ti/Pt Resistive Random Access Memory (RRAM) device that facilitates event detection for edge vision sensors without the requirement for power-intensive Analog-to-Digital Converters (ADCs). The platform is supported with a hardware-friendly background comparison technique providing adjustable precision that allows for a dynamic balance between accuracy and efficiency at runtime. Our device-to-architecture simulation results demonstrate that the proposed platform achieves on average 66% and 63% energy saving over STT-MRAM and SOT-MRAM counterparts due to utilizing the ADC-less method.",
author = "Mehrdad Morsali and Sepehr Tabrizchi and Velpula, {Ravi Teja} and {Sankar Muthu}, {Mano Bala} and {Trung Nguyen}, {Hieu Pham} and Mohsen Imani and Arman Roohi and Shaahin Angizi",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2024 ; Conference date: 01-07-2024 Through 03-07-2024",
year = "2024",
doi = "10.1109/ISVLSI61997.2024.00067",
language = "English (US)",
series = "Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI",
publisher = "IEEE Computer Society",
pages = "331--336",
editor = "Himanshu Thapliyal and Jurgen Becker",
booktitle = "2024 IEEE Computer Society Annual Symposium on VLSI",
address = "United States",
}