Next Event

Date: September 6th, 2019
CASS-SCV Artificial Intelligence for Industry (AI4I) Forum – Fall 2019



Want to volunteer?

The IEEE SCV CAS chapter is seeking volunteers to help with the organization of technical meetings. Please contact us.


SCV-CAS Mailing List

To subscribe or unsubcribe, please visit the IEEE SCV-CAS list.

Analog and Mixed-Signal Designs for Machine Learning and Optimizations: Lecture by Dr. A. Raychowdhury

Date: September 20th, 2018


IEEE Silicon Valley Solid-State Circuits Society (SSCS)

Cosponsored by

IEEE Silicon Valley Circuits and Systems Society (CASS)

IEEE Silicon Valley Signal Processing Society (SPS)

“Analog and Mixed-Signal Designs for Machine Learning and Optimizations”

Prof. Arijit Raychowdhury
ON Semiconductor Associate Professor
School of Electrical and Computer Engineering
Georgia Institute of Technology, Atlanta, GA

Time: September 20 (Thursday) evening 6:00PM-8:00PM
Networking and Refreshments: 6:00 PM – 6:30 PM
Technical Talk: 6:30 PM – 8:00 PM

Registration Link:

Click here to register.


Texas Instruments Silicon Valley Auditorium 2900 Semiconductor Dr., Building E, Santa Clara, CA 95051 Directions and Map (to locate Building E).

The seminar is FREE and donation is accepted for refreshments (FREE SSCS/CAS/SPS/CS members/$2 IEEE members/$5 non-members)
Eventbrite registration is required for everyone to attend the talk.


Analog and mixed-signal systems offer unique opportunities for computing by harnessing the complex interactions of simple elements such as oscillators or spike generators. This is possible, when such dynamics can be programmed, controlled, and observed. In this talk, I will present some of our work where we are exploring analog and mixed-signal processing in CMOS. I will discuss applications of such systems in solving inverse problems, distributed optimizations (convex and combinatorial) and machine learning. In particular, I will talk about implementation of such dynamics in mixed-signal CMOS, including a recent demonstration of reinforcement learning for energy-constrained edge devices. I will conclude with a discussion on the promise of analog and continuous-time systems for solving computationally hard problems and discuss our recent work that connects coupled oscillatory networks and algebraic graph theory.


Arijit Raychowdhury is an Associate Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology where he joined in January, 2013. He currently holds the ON Semiconductor Jr Professorship and is the Associate Director of the Center for Co-Design of Chips, Packaging and Systems. He received his Ph.D. degree in Electrical and Computer Engineering from Purdue University in 2007. Prior to joining academia, he was a staff scientist at Intel’s Circuit Research Labs for five years where he worked on mixed-signal and digital designs for energy-efficiency sensors and compute nodes. Before that, he spent one and a half years at Texas Instruments where he worked on developing the world’s first adaptive echo cancellation unit for DSL modems, which received the EDN industrial design award. Dr. Raychowdhury holds more than 25 international patents and has published over 150 articles in journals and refereed conferences. Over the years he has won multiple best paper awards and fellowships, including the DAC Innovator under-40 Award, 2018; Georgia Tech’s Outstanding Young Faculty Award, 2018; Intel Early Career Fellowship, NSF CISE CRII Award, 2016.

  • September 2018
    M T W T F S S
    « Aug   Oct »