IEEE YP Seminar: Open-Source AI for Analog Correction: From RF Power Amplifiers to Energy-Efficient Silicon

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Please join the IEEE Boston MTT/AP-S Chapter for a free in-person Young Professional seminar with Yizhuo Wu entitled "Open-Source AI for Analog Correction: From RF Power Amplifiers to Energy-Efficient Silicon".

All visitors must check in at the first floor of the HUB before crossing over to Building 6.

We will have a social period from 5-6pm, followed by the technical seminar from 6-7pm.

Refreshments will be provided. Please note if you have any dietary restrictions.


  Date and Time

  Location

  Hosts

  Registration


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  • 1 Analog Way
  • Analog Devices, Inc.
  • Wilmington, Massachusetts
  • United States
  • Building: Building #6
  • Room Number: Boston Conference Room
  • Contact Event Hosts
  • Starts 19 May 2026 04:00 PM UTC
  • Ends 04 June 2026 04:00 AM UTC
  • No Admission Charge

  Speakers

Yizhuo of Delft University of Technology

Topic:

Open-Source AI for Analog Correction: From RF Power Amplifiers to Energy-Efficient Silicon

RF PAs dominate the energy budget of modern base stations and Wi-Fi access points, yet their efficiency-oriented designs aggravate nonlinearities. At the same time, next-generation standards demand tighter linearity and modulation accuracy under wide bandwidths, making robust digital predistortion increasingly critical. For practical deployment, two considerations are important: 1) linearity, meeting stringent ACPR and EVM targets even under quantization; 2) energy, keeping inference and update power within tight radio back-end budgets.  This talk will discuss the OpenDPD framework and the role of open-source AI in bridging the gap between algorithm design and silicon deployment for analog/RF systems.

Biography:

Yizhuo Wu received her B.Sc. degree in microelectronics from UESTC, Chengdu, China, in 2021 and her M.Sc. degree in microelectronics at TU Delft in 2023. She is now a Ph.D. student supervised by Dr. Chang Gao in the Lab of Efficient Machine Intelligence. Her research focuses on co–designed software–hardware AI for wireless signal processing, aiming to develop energy-efficient solutions for high-frequency signal processing tasks. She is a recipient of the 2026 IEEE MTT-S Graduate Student Fellowship.

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