The IAP Harvard/MIT Workshop on the Future of Cloud Computing Applications and Infrastructure was conducted on October 7, 2021. This was the fourth Cloud Workshop hosted by Harvard and MIT.

The Workshop focus was on TinyML, Sustainable AI and Sparse Compute.

October 7, 2021 - 9am-3pm PDT (Online)

Organizers: Prof. Daniel Sanchez (MIT), Prof. Vijay Janapa Reddi (Harvard), and Prof. David Brooks (Harvard).

Agenda

Videos of Presentations

Session 1: TinyML (Chair: Daniel Sanchez)

9:05 - 9:50 (45 mins): Vijay Janapa Reddi, Harvard, "Democratizing TinyML: Generalization, Standardization and Automation"

9:50 - 10:30 (40 mins): Meng Li, Facebook Reality Lab, "Efficient Audio-Visual Understanding on AR Devices"

10:30 - 11:15 (45 mins): Song Han, MIT, "Today's AI is Too Big"

11:15 - 11:45 (30 mins): Evgeni Gousev, Qualcomm, "The TinyML Phenomenon: Current Progress and Opportunities Ahead"

11:45 - 12:30: Lunch Break (Gather Online)

Session 2: Sparse Compute and Sustainable AI (Chair: Vijay Janapa Reddi)

12:30 - 1:15 (45 mins): Joel Emer, MIT and Nvidia, "Exploiting Sparsity in Deep Neural Network Accelerator Hardware"

1:15 - 1:45 (30 mins): David Brooks, Harvard, "Architecting Systems for Sustainable AI Computing"

1:45 - 2:15 (30 mins): Fredrik Kjolstad, Stanford, "Compiling Sparse Array Programming Languages"

2:15 - 2:45 (30 mins): Daniel Sanchez, MIT, "Architectural Support for Efficient Sparse Computation"

2:45 - 2:50 (5 mins): Wrap Up

Abstracts and Bios

David Brooks, Harvard

Title: Architecting Systems for Sustainable AI Computing Abstract: The past decade has seen incredible advances in AI largely driven by improved algorithms and models that can harness large amounts of training data. However, these advances are underpinned by enormous consumption of computational resources for training and inference at scale. As society embraces the benefits of AI across nearly all industries, researchers must provide a path toward sustainable AI computing. ...
Bio: David Brooks is the Haley Family Professor of Computer Science ...

Joel Emer, MIT and Nvidia

Title: Exploiting Sparsity in Deep Neural Network Accelerator Hardware Abstract: Recently it has increasingly been observed that exploiting sparsity in hardware for linear algebra computations can result in significant performance improvements. ...
Bio: Joel Emer has held various research and advanced development positions ...

Evgeni Gousev, Qualcomm

Title: The TinyML Phenomenon: Current Progress and Opportunities Ahead Abstract: Data fuels the digital revolution. Is there a reliable, fast, energy efficient, privacy preserving ...
Bio: Evgeni Gousev is a Senior Director of Qualcomm AI Research ...

Song Han, MIT

Title: Today's AI is Too Big Abstract: Today’s AI is too big. Deep neural networks demand extraordinary levels of data ...
Bio: Song Han is an assistant professor at MIT’s EECS ...

Fredrik Kjolstad, Stanford

Title: Compiling Sparse Array Programming Languages Abstract: We present the first compiler for the general class of sparse array programming languages ...
Bio: Fredrik Kjolstad is an Assistant Professor at Stanford University ...

Meng Li, Facebook AI Research Lab

Title: Efficient Audio-Visual Understanding on AR Devices Abstract: Augmented reality (AR) is a set of technologies that will fundamentally change ...
Bio: Meng Li is currently a Staff AI Research Scientist at Facebook ...

Vijay Janapa Reddi, Harvard

Title: Democratizing TinyML: Generalization, Standardization and Automation Abstract: Tiny machine learning (ML) is poised to drive enormous growth within the IoT hardware and software industry ...
Bio: Prof. Janapa Reddi is an Associate Professor in John A. Paulson School of Engineering ...

Daniel Sanchez, MIT

Title: Architectural Support for Efficient Sparse Computation Abstract: Computer systems have long been designed and optimized for regular computations, ...
Bio: I am an Associate Professor at MIT's Electrical Engineering and Computer Science Department ...