Shaahin Angizi
New Jersey Institute of Technology, USA
Advancing Energy Efficiency: Next Generation Domain-Specific In-Sensor and In-Memory Accelerators, Bridging from Circuit to Algorithm
Abstract
Internet of Things (IoT) devices are projected to attain an $1100B market by 2025, with a web of interconnection projected to comprise approximately 75+ billion IoT devices. The large number of IoTs consist of sensory systems that enable massive data collection from the environment and people. However, considerable portions of the captured sensory data are redundant and unstructured. Data conversion of such large raw data, stored in volatile memories, transmission, and computation in the cloud, impose high energy consumption, latency, and a memory bottleneck at the edge. Therefore, data-centric edge devices with high-speed, low-power, and normally-off computing domain-specific architectures should be explored and developed to overcome these issues. Motivated by the aforementioned concerns, in this talk, I will be focusing on cross-layer (device/ circuit/ architecture/ application) co-design of energy-efficient and high-performance processing-in-sensor and processing-in-memory platforms for implementing complex AI and machine learning tasks. I explain how to leverage innovations from circuits and architecture to integrate sensors, memory, and logic to break the existing memory and power walls.
Biography
Shaahin Angizi is an Assistant Professor in the Department of Electrical and Computer Engineering, at the New Jersey Institute of Technology (NJIT), and the director of the Advanced Circuit-to-Architecture Design Laboratory (ACAD Lab). He received his Ph.D. in Electrical Engineering at the School of Electrical, Computer and Energy Engineering, Arizona State University (ASU) in 2021. His research interests include the cross-layer design of energy-efficient and high-performance processing-in-memory, processing-in-sensor, and ASIC platforms to enhance complex machine-learning tasks, bioinformatics, and graph processing. He has authored and co-authored 120+ research articles in top-tier journals and conferences such as IEEE TCAD, IEEE TC, IEEE TCASI, IEEE TETC, DAC, DATE, ICCAD, ASP-DAC, etc. He received the “Best Ph.D. research award – 1st place” at the Design Automation Conference’s (DAC) Ph.D. forum in 2018, two “Best Paper” awards at the IEEE Computer Society Annual Symposium on Very Large-Scale Integration (ISVLSI) in 2017 and 2018, and two “Best Paper” awards at the ACM Great Lakes Symposium on VLSI (GLSVLSI) in 2019 and 2023. For more information, please see http://shaahinangizi.com.
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