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Can We Make a Brain-Scale Neuromorphic Nanochip? | with Professor Sung-Mo “Steve” Kang
For over half a century, the semiconductor industry has made remarkable contributions to benefit society globally. The phenomenal progress in VLSI through CMOS technology scaling has made unimaginable things a reality, and Moore’s law has served as a can-do marching order. However, the semiconductor industry has been facing the end of Moore's law and the limitation of the von Neumann architecture, especially considering the power bottleneck and energy efficiency. In the meantime, applications of artificial intelligence (AI) have far outpaced Moore’s law in chip development, thus creating an increasingly larger gap between the user demand and the supply that the semiconductor industry can deliver.
In this talk, we will review the state of the art in CMOS VLSI technology and the unique roles of memristor technologies that can be leveraged in developing upscaled AI neural networks. Biologically inspired, spiking neural networks (SNNs) can perform brain-like neuromorphic computing and unsupervised learning with high energy efficiency. Although numerous aspects of the human brain remain unknown, open-source memristor circuit designs, along with open-source software, may help facilitate the development of micro/nano-electronic systems that emulate brain functions.
Biography:
Sung-Mo “Steve” Kang is a Distinguished Professor Emeritus and Research Professor of the Baskin School of Engineering, UC Santa Cruz, Chancellor Emeritus of UC Merced, and President Emeritus of KAIST. He has received honors, including a multitude of best paper awards, the Silicon Valley Engineering Hall of Fame induction, Alexander von Humboldt Senior US Scientists Award, IEEE Millennium Medal, IEEE Mac Van Valkenburg Circuits and Systems (CAS) Society Award, IEEE CAS Society Technical Excellence Award, the US Semiconductor Research Corporation (SRC) Technical Excellence Award, IEEE Leon K. Kirchmayer Graduate Teaching Technical Field Award, IEEE CAS Society John Choma Education Award, Chang-Lin Tien Education Leadership Award, and distinguished alumni awards from UC Berkeley, The University at Buffalo, Fairleigh Dickinson University, and Yonsei University.
In this talk, we will review the state of the art in CMOS VLSI technology and the unique roles of memristor technologies that can be leveraged in developing upscaled AI neural networks. Biologically inspired, spiking neural networks (SNNs) can perform brain-like neuromorphic computing and unsupervised learning with high energy efficiency. Although numerous aspects of the human brain remain unknown, open-source memristor circuit designs, along with open-source software, may help facilitate the development of micro/nano-electronic systems that emulate brain functions.
Biography:
Sung-Mo “Steve” Kang is a Distinguished Professor Emeritus and Research Professor of the Baskin School of Engineering, UC Santa Cruz, Chancellor Emeritus of UC Merced, and President Emeritus of KAIST. He has received honors, including a multitude of best paper awards, the Silicon Valley Engineering Hall of Fame induction, Alexander von Humboldt Senior US Scientists Award, IEEE Millennium Medal, IEEE Mac Van Valkenburg Circuits and Systems (CAS) Society Award, IEEE CAS Society Technical Excellence Award, the US Semiconductor Research Corporation (SRC) Technical Excellence Award, IEEE Leon K. Kirchmayer Graduate Teaching Technical Field Award, IEEE CAS Society John Choma Education Award, Chang-Lin Tien Education Leadership Award, and distinguished alumni awards from UC Berkeley, The University at Buffalo, Fairleigh Dickinson University, and Yonsei University.