Brain-Inspired Computing: Adventure from Beyond CMOS Technologies to Beyond von Neumann Architectures ICCAD Special Session Paper

作者
Hussam Amrouch,Jian-Jia Chen,Kaushik Roy,Yuan Xie,Indranil Chakraborty,Wenqin Huangfu,Ling Liang,Fengbin Tu,Cheng Wang,Mikail Yayla
标识
DOI:10.1109/iccad51958.2021.9643488
摘要

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.

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