Analog and Mixed Signal Circuits and Systems for Emerging Applications

#machine-learning #electronics #quantum-algorithm #energy-efficient-computing #cmos #merging
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IEEE CASS Distinguished Lecturer Prof. Bibhu Datta Sahoo 


Abstract

Quantum Computing, having the ability to exponentially enhance the raw computing power, and Artificial Intelligence (AI), having the ability to impart unprecedented intelligence to connected devices through algorithms that learn, are the two key technologies of the 21st century. Although novel devices can significantly advance the field of quantum computing, conventional CMOS based analog and mixed signal circuits can enable quantum computing using classical op amp based circuits. AI algorithms on the other hand are tolerant to errors in computation, thereby enabling approximate and low-precision computing which has resulted in the resurrection of more than half-a-century old analog computing.

This talk would present various analog computing techniques that could enable AI algorithms or more specifically machine learning (ML) algorithms. The talk could also delve into mixed-sginal computing where it will dive into the details of in-memory computing as well as adoption of novel memory devices, viz., memristors, to enable energy efficient computing. The talk would also present some classical analog hardware for emulating quantum algorithms, like Grover's search algorithm, for quantum computing systems.



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  • University of California, Santa Cruz
  • Santa Cruz, California
  • United States
  • Building: E2
  • Room Number: 192
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  • Starts 10 October 2025 07:00 AM UTC
  • Ends 13 October 2025 07:00 AM UTC
  • No Admission Charge


  Speakers

Bibhu

Topic:

Analog and Mixed Signal Circuits and Systems for Emerging Applications

Abstract

Quantum Computing, having the ability to exponentially enhance the raw computing power, and Artificial Intelligence (AI), having the ability to impart unprecedented intelligence to connected devices through algorithms that learn, are the two key technologies of the 21st century. Although novel devices can significantly advance the field of quantum computing, conventional CMOS based analog and mixed signal circuits can enable quantum computing using classical op amp based circuits. AI algorithms on the other hand are tolerant to errors in computation, thereby enabling approximate and low-precision computing which has resulted in the resurrection of more than half-a-century old analog computing.

This talk would present various analog computing techniques that could enable AI algorithms or more specifically machine learning (ML) algorithms. The talk could also delve into mixed-sginal computing where it will dive into the details of in-memory computing as well as adoption of novel memory devices, viz., memristors, to enable energy efficient computing. The talk would also present some classical analog hardware for emulating quantum algorithms, like Grover's search algorithm, for quantum computing systems.

Biography:

Bibhu Datta Sahoo received the B.Tech. degree in electrical engineering from the Indian Institute of Technology Kharagpur, Kharagpur, India, in 1998, the M.S.E.E. degree from the University of Minnesota, Minneapolis, MN, USA, in 2000, and the Ph.D.E.E. degree from the University of California, Los Angeles, CA, USA, in 2009. From 2000 to 2006, he was with Broadcom Corporation, Irvine, CA, USA. From December 2008 to February 2010, he was with Maxlinear Inc., Carlsbad, CA, USA, where he was involved in designing integrated circuits for CMOS TV tuners. From March 2010 to November 2010, he was a Post-Doctoral Researcher with the University of California, Los Angeles, CA, USA. From December 2011 to April 2015, he was an Associate Professor with the Department of Electronics and Communication Engineering, Amrita University, Amritapuri, India. From August 2017 to August 2023 he has been Associate Professor in the Department of Electronics and Electrical Communication Engineering at Indian Institute of Technology Kharagpur, Kharagpur, India. Since September 2023 he has been a Professor in the Department of Electrical Engineering at University at Buffalo, State University of New York. His research interests include data converters, signal processing, and analog and mixed signal circuit design.

He received the 2008 Analog Devices Outstanding Student Designer Award and was the co-recipient of the 2013 CICC Best Paper Award. He was the Associate Editor of IEEE Transactions on Circuits and Systems-II from August 2014 to December 2015.

IEEE CASS Recognitions:





Agenda

Date and Time

 

Date: Monday, October 13
Time: 10:40 AM – 11:45 AM

 

 

Location


Place: E2-192, UC Santa Cruz

Santa Cruz, California, USA