Chips & Compilers Symposium at MLSys ‘22

Modern machine learning systems call for scalable and efficient solutions to both gigantic model training and flexible model inference. This requires joint design and optimization between hardware and software to take full advantage of the provided resource. We have observed active development in this field in the past few years, ranging from machine learning specific hardware and compiler, to customized optimization towards modern machine learning workloads. This symposium aims to bring together experts from the field of computer architecture and compilers to share first-hand experiences, lessons, and best practices, of designing ML workload specific chips and compilers. Like last year, the symposium consists of invited talks by domain experts from both academia and industry.

Time and location

Mission Ballroom MR3, Santa Clara Convention Center

9 am, September 1, 2022

Speakers

Dave Patterson

Distinguished Engineer at Google

Fredrik Kjolstad

Assistant Professor at Stanford

Hadi Esmaeilzadeh

Associate Professor at UCSD and Co-Founder and CTO at Protopia AI

Jianhui Li

Senior Principal Engineer at Intel

Peng Wu

Engineering Manager at Meta

Ron Diamant

Senior Principal Engineer at AWS

Song Han

Associate Professor at MIT

Tobias Edler von Koch

Senior Compiler Engineer at AWS

Vijay Janapa Reddi

Associate Professor at Harvard

Yuanzhong Xu

Staff Software Engineer at Google

Zhihao Jia

Assistant Professor at CMU

Organizers

Yida Wang

Principal Scientist at AWS

Gennady Pekhimenko

Assistant Professor at University of Toronto and Vector Institute

Agenda

Morning Session

Time

Talk Title: Speaker

9:00–9:05

Opening: Organizers

9:05–9:50

A Decade of Machine Learning Accelerators: Lessons Learned and Carbon Footprint: Dave Patterson

9:50–10:20

Automatically Discovering Machine Learning Optimizations: Zhihao Jia

10:20–10:40

Break

10:40–11:10

What makes PyTorch beloved makes it hard to compile – the nuanced design of PyTorch Compilers: Peng Wu

11:10–11:40

Tiny Machine Learning: Vijay Janapa Reddi

11:40–12:10

AI for Better AI: Hadi Esmaeilzadeh

12:10–13:15

Lunch (Will be provided)

Afternoon Session

Time

Talk Title: Speaker

13:15–14:00

Optimizing ML workloads across the stack with AWS Trainium: Ron Diamant & Tobias Edler von Koch

14:00–14:30

GSPMD: generalized parallelism for large models as shared compiler infrastructure: Yuanzhong Xu

14:30–15:00

Break

15:00–15:30

Software and Hardware for Sparse ML: Fred Kjolstad

15:30–16:00

On-Device Training Under 256KB Memory: Song Han

16:00–16:30

Generating efficient code for deep neural network: Jianhui Li

See also