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Shaahin Angizi

Assistant Professor @ NJIT

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I (Shaahin Angizi) am an Assistant Professor in the Department of Electrical and Computer Engineering, New Jersey Institute of Technology (NJIT), Newark, NJ, USA, and the director of the ACAD Lab. 

I completed my doctoral studies in Electrical Engineering at the School of Electrical, Computer and Energy Engineering, Arizona State University (ASU), Tempe, AZ.

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My group research interests include the cross-layer (device/circuit/architecture/algorithm/application) co-design of energy-efficient and high-performance systems with the following directions:

  • Accelerator Design for Big Data Applications: Deep Learning, Bioinformatics, Graph Processing, etc.

  • In-Memory Computing with Volatile & Non-Volatile Memories

  • Adaptive Learning for Collaborative In-Edge AI Computing IoT Systems

  • Low Power and Area-Efficient In-Sensor Computing for IoT 

  • Hardware Security Solution for Emerging Non-Volatile Memories

  • Low power VLSI circuits

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Research Assistant Positions Available
I am currently looking for highly motivated and self-driven Master & Ph.D. students for 2022!

(Click for more info)

News:​

  • [Jan 2022]: Our paper “SCiMA: a Generic Single-Cycle Compute-in-Memory Acceleration Scheme for Matrix Computations” is accepted to ISCAS'22.

  • [Dec 2021]: Our paper “ReFACE: Efficient Design Methodology for Acceleration of Digital Filter Implementations” is accepted to ISQED'22.

  • [Nov 2021]: I am invited to serve as a TPC member for VLSID 2022.

  • [Oct 2021]: I am invited to serve as a TPC member for DAC 2022.

  • [Oct 2021]: I am invited to serve as a TPC member for ISQED 2022.

  • [Aug 2021]: Our paper “MeF-RAM: A New Non-Volatile Cache Memory Based on Magneto-Electric FET” is accepted to ACM Transactions on Design Automation of Electronic Systems (TODAES).

  • [Aug 2021]: "Neuromorphic Computing: From Material to Algorithm (NeuMA)" workshop program at IEEE IGSC 2021 is now finalized.

  • [July 2021]: Our paper “RNSiM: Efficient Deep Neural Network Accelerator Using Residue Number Systems” is accepted to the 40th International Conference On Computer Aided Design (ICCAD), 2021.

  • [April 2021]: Our paper “Processing-in-Memory Acceleration of MAC-based Applications Using Residue Number System: A Comparative Study” in collaboration with UNL is accepted to the 31st edition of GLSVLSI.

  • [Feb 2021]: Two papers are accepted to 58th DAC.

Selected Awards and Distinctions:

2019   Best Paper Award of 2019 ACM Great Lakes Symposium on VLSI (GLSVLSI), Washington, D.C., USA​

2018   Best Ph.D. Research Award (1st-place) of 2018 Ph.D. Forum at Design Automation Conference (DAC), San Francisco, CA, USA​

2018   Best Paper Award of 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Hong Kong, China​

2018   Featured Paper of October-December 2018 issue of IEEE Transactions on Emerging Topics in Computing​

2017   Best Paper Award of 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Bochum, Germany​

Academic Activities and Service:

Dr. Angizi has authored and co-authored more than 70 research articles in top-ranked international journals and top-tier electronic design automation conferences such as IEEE TNANO, IEEE TCAD, IEEE TC, IEEE TCASI, IEEE TETC, DAC, DATE, ICCAD, ASP-DAC, etc. He received the “Best Ph.D. research award” at the Design Automation Conference’s Ph.D. forum in 2018, two “Best Paper” awards at the IEEE Computer Society Annual Symposium on Very Large-Scale Integration (VLSI) in 2017 and 2018, and a “Best Paper” award at the ACM Great Lakes Symposium on VLSI in 2019.

He has served as a technical reviewer for over 30 international journals/conferences, such as IEEE TC, TVLSI, TCAD, TNANO, TCAS, ESL, ACM JETC, MICRO, DAC, ASP-DAC, DATE, ICCAD, ICCD, GLSVLSI, ISVLSI, etc.