TY - GEN
T1 - Brain-Inspired Computing
T2 - 40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021
AU - Amrouch, Hussam
AU - Chen, Jian Jia
AU - Roy, Kaushik
AU - Xie, Yuan
AU - Chakraborty, Indranil
AU - Huangfu, Wenqin
AU - Liang, Ling
AU - Tu, Fengbin
AU - Wang, Cheng
AU - Yayla, Mikail
N1 - Publisher Copyright:
©2021 IEEE
PY - 2021
Y1 - 2021
N2 - The goal of this special session paper is to introduce and discuss different breakthrough technologies as well as novel architectures and how they together may reshape the future of Artificial Intelligent. Our aim is to provide a comprehensive overview on the latest advances in brain-inspired computing and how the latter can be realized when emerging technologies, using beyond-CMOS devices, are coupled with novel computing paradigms that go beyond von Neumann architectures. Different emerging technologies like Ferroelectric Field-Effect Transistor (FeFET), Phase Change Memory (PCM), and Resistive RAM (ReRAM) are discussed, demonstrating their promising capability in building neuromorphic computing architectures that are inspired by nature. In addition, this special session paper discusses various novel concepts such as Logic-in-Memory (LIM), Processing-in-Memory (PIM), and Spiking Neural Networks (SNNs) towards exploring the far-reaching consequences of beyond von Neumann computing on accelerating deep learning. Finally, the latest trends in brain-inspired computing are summarized into algorithm, technology, and application-driven innovations towards comparing different PIM architectures.
AB - The goal of this special session paper is to introduce and discuss different breakthrough technologies as well as novel architectures and how they together may reshape the future of Artificial Intelligent. Our aim is to provide a comprehensive overview on the latest advances in brain-inspired computing and how the latter can be realized when emerging technologies, using beyond-CMOS devices, are coupled with novel computing paradigms that go beyond von Neumann architectures. Different emerging technologies like Ferroelectric Field-Effect Transistor (FeFET), Phase Change Memory (PCM), and Resistive RAM (ReRAM) are discussed, demonstrating their promising capability in building neuromorphic computing architectures that are inspired by nature. In addition, this special session paper discusses various novel concepts such as Logic-in-Memory (LIM), Processing-in-Memory (PIM), and Spiking Neural Networks (SNNs) towards exploring the far-reaching consequences of beyond von Neumann computing on accelerating deep learning. Finally, the latest trends in brain-inspired computing are summarized into algorithm, technology, and application-driven innovations towards comparing different PIM architectures.
KW - DNN
KW - Emerging technology
KW - FeFET
KW - Neuromorphic
KW - PCM
KW - Photonic
KW - Processing-in-Memory
KW - ReRAM
KW - SNN
UR - http://www.scopus.com/inward/record.url?scp=85124133124&partnerID=8YFLogxK
U2 - 10.1109/ICCAD51958.2021.9643488
DO - 10.1109/ICCAD51958.2021.9643488
M3 - Conference contribution
AN - SCOPUS:85124133124
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
BT - 2021 40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 November 2021 through 4 November 2021
ER -