TALK    [MERL Seminar Series 2022] Analog CMOS Computing Chips for Fast and Energy-Efficient Solution of PDE Systems

Date released: March 15, 2022


  •  TALK    [MERL Seminar Series 2022] Analog CMOS Computing Chips for Fast and Energy-Efficient Solution of PDE Systems
    (Learn more about the MERL Seminar Series.)
     
  • Date & Time:

    Tuesday, March 15, 2022; 1:00 PM EDT

  • Abstract:

    Analog computers are making a comeback. In fact, they are taking the world by storm. After decades of “analog computing winter” that followed the invention of the digital computing paradigm in the 1940s, classical physics-based analog computers are being reconsidered for improving the computational throughput of demanding applications. The research is driven by exponential growth in transistor densities and bandwidths in the integrated circuits world, which in turn, has led to new possibilities for the creative circuit designer. Fast analog chips not only furnish communication/radar front-ends, but can also be used to accelerate mathematical operations. Most analog computer today focus on AI and machine learning. E.g., analog in-memory computing plays an exciting role in AI acceleration because linear algebra operations can be mapped efficiently to compute in memory. However, many scientific computing tasks are built on linear and non-linear partial differential equations (PDEs) that require recursive numerical PDE solution across spatial and temporal dimensions. The adoption of analog parallel processors that are built around speed vs power efficiency vs precision trade-offs available from circuitry for PDE solution require new research in computer architecture. We report on recent progress on CMOS based analog computers for solving computational electromagnetics and non-linear pressure wave equations. Our first analog computing chip was measured to be more than 400x faster than a top-of-the-line NVIDIA GPU while consuming 1000x less power for elementary computational electromagnetics computations using finite-difference time-domain scheme.

  • Speaker:

    Arjuna Madanayake
    Florida International University

    Arjuna Madanayake is an Associate Professor of Electrical and Computer Engineering at Florida International University. His received the Ph.D. degree in Electrical Engineering from the University of Calgary, Canada, in 2008, and the B.Sc. degree in Electronic and Telecommunication Engineering from the University of Moratuwa, Sri Lanka, in 2002. Dr. Madanayake’s research interests include antenna array processing, phased-array, multi-dimensional signal processing, digital filter design, light field signal processing, computer architecture, digital VLSI, FPGA systems, software defined radio, mm-wave and 5G/6G communications, radar signal processing, full-duplex/STAR for MIMO, computer arithmetic, digital arithmetic circuits, blockchain based spectrum management, machine learning for RF systems, intelligent circuits, analog computing, mixed-signal electronics, and digital AI accelerators for advanced autonomy.

  • Research Areas:

    Applied Physics, Electronic and Photonic Devices, Multi-Physical Modeling