Stanford MLSys Seminar Episode 57: Vijay Janapa Reddi [Th, 1.35-2.30pm PT]

[Karan Goel]

Hi everyone,

We're back with the fifty-seventh episode of the MLSys Seminar on Thursday from 1.35-2.30pm PT. 

We'll be joined by Vijay Janapa Reddi, who will talk about tiny machine learning. The format is a 30 minute talk followed by a 30 minute podcast-style discussion, where the live audience can ask questions.

Livestream Link: https://www.youtube.com/watch?v=489HEmRXzOE

Guests: Vijay Janapa Reddi

Title: Tiny Machine Learning

Abstract: Tiny machine learning (TinyML) is a fast-growing field at the intersection of ML algorithms and low-cost embedded systems. TinyML enables on-device analysis of sensor data (vision, audio, IMU, etc.) at ultra-low-power consumption (<1mW). Processing data close to the sensor allows for an expansive new variety of always-on ML use-cases that preserve bandwidth, latency, and energy while improving responsiveness and maintaining privacy. This talk introduces the vision behind TinyML and showcases some of the interesting applications that TinyML is enabling in the field, from wildlife conservation to supporting public health initiatives. Yet, there are still numerous technical hardware and software challenges to address. Tight memory and storage constraints, MCU heterogeneity, software fragmentation and a lack of relevant large-scale datasets pose a substantial barrier to developing TinyML applications. To this end, the talk touches upon some of the research opportunities for unlocking the full potential of TinyML.
Bio: Vijay Janapa Reddi is an Associate Professor at Harvard University, VP and a founding member of MLCommons (mlcommons.org), a nonprofit organization aiming to accelerate machine learning (ML) innovation for everyone. He also serves on the MLCommons board of directors and is a Co-Chair of the MLCommons Research organization. He co-led the MLPerf Inference ML benchmark for data center, edge, mobile and IoT systems. Dr. Janapa-Reddi is a recipient of multiple honors and awards, including the National Academy of Engineering (NAE), Gilbreth Lecturer Honor and IEEE TCCA Young Computer Architect Award. He is passionate about widening access to applied machine learning for STEM, Diversity, and using AI for social good. He designed the Tiny Machine Learning (TinyML) series on edX, a massive open online course (MOOC) that sits at the intersection of embedded systems and ML that tens of thousands of global learners access and audit free of cost. He received a Ph.D. in CS from Harvard University, an M.S. from the University of Colorado at Boulder and a B.S from Santa Clara University.

See you all there!

Best,

Karan