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Abstract
Generative AI applications with their ability to produce natural
language, computer code and images are transforming all aspects
of society. These applications are powered by huge foundation
models such as GTP-3 which are trained on massive unlabeled
datasets. Foundation models have 10s of billions of parameters
and have obtained state-of-the-art quality in natural language
processing, vision and speech applications. These models are
computationally challenging because they require 100s of
petaFLOPS of computing capacity for training and inference.
Future foundation models will have even greater capabilities
provided by more complex model architectures with longer sequence
lengths, irregular data access (sparsity) and irregular control
flow. In this talk I will describe how the evolving
characteristics of foundation models will impact the design of
the optimized computing systems required for training and
serving these models. A key element of improving the performance
and lowering the cost of deploying future foundation models
will be optimizing the data movement within the model using
specialized hardware. In contrast to human-in-the-loop
applications such as conversational AI, an emerging application
of foundation models is in continuous batch processing
applications that operate without human supervision. I will
describe how continuous batch processing and real-time machine
learning can be used to create an intelligent network data plane.
Speaker
Kunle Olukotun is the Cadence Design Professor of
Electrical Engineering and Computer Science at Stanford
University. Olukotun is a pioneer in multicore processor design
and the leader of the Stanford Hydra chip multiprocessor (CMP)
research project. He founded Afara Websystems to develop
high-throughput, low-power multicore processors for server
systems. The Afara multi-core processor, called Niagara, was
acquired by Sun Microsystems and now powers Oracle's SPARC-based
servers. In 2017, Olukotun co-founded SambaNova Systems, a
Machine Learning and Artificial Intelligence company, and
continues to lead as their Chief Technologist. Olukotun is the
Director of the Pervasive Parallel Lab and a member of the Data
Analytics tor What's Next (DAWN) Lab, developing infrastructure
for usable machine learning. He is a member of the National
Academy of Engineering, an ACM Fellow, and an IEEE Fellow for
contributions to multiprocessors on a chip design and the
commercialization of this technology. He also received
the Harry H. Goode Memorial Award. Olukotun received his Ph.D.
in Computer Engineering from The University of Michigan.
Title
ISCA50, a birthday panel: celebrating the past and looking to the future.
Moderator
Parthasarathy Ranganathan (Google)
Panelists
Dave Patterson (Google),
Margaret Martonosi (Princeton University and NSF),
Todd Austin (University of Michigan and Agita Labs),
Onur Mutlu (ETH Zurich),
Luis Ceze (University of Washington and OctoML),
and Thierry Tambe (Harvard University)
Abstract
Throughout human history, society has faced great opportunities
and challenges, and has used its available toolkit to navigate
them. Today, many of the global opportunities and challenges we
face will require the full engagement of the computing innovation
and research community to take on. Resiliently navigating climate
trends will require computing techniques and systems to model the
future, as well as innovative techniques to mitigate carbon
footprint by employing telepresence, optimizing logistics, and
more. Another grand challenge of our era is the ability for us
as individuals and as groups to communicate with each other in a
way that upholds accuracy, integrity, privacy, and trust.
The computing research and innovation ecosystem has the power to
help. This talk will discuss how the different elements of this
ecosystem — academia, industry, professional organizations, and
governments — can work together to meet these challenges.
It will be a call to action on how we can best navigate the next
decade and beyond to do so.
Speaker
Margaret Martonosi is the US National Science Foundation’s (NSF)
Assistant Director for Computer and information Science and
Engineering (CISE). With an annual budget of more than $1B, the
CISE directorate at NSF has the mission to uphold the Nation’s
leadership in scientific discovery and engineering innovation
through its support of fundamental research and education in
computer and information science and engineering as well as
transformative advances in research cyberinfrastructure.
While at NSF, Dr. Martonosi is on leave from Princeton University
where she is an endowed professor of Computer Science.
Dr. Martonosi's research interests are in computer architecture
and hardware-software interface issues in both classical and
quantum computing systems. Dr. Martonosi is a member of the
National Academy of Engineering and a Fellow of the ACM and IEEE.
Abstract
TBD
Panelists
TBD
Abstract
For decades now cryptographic tools and models have been
developed to transform platforms controlled by worst case
adversaries to trustworthy platforms. In this talk I will describe
how to use a general cryptographic recipe and specific
cryptographic tools to build trust in various phases of the
machine learning pipelines or prove that at times it is
impossible to achieve. We will touch on achieving verification,
robustness and privacy. If time permits, we will show how
cryptographic tools can be brought to build trust in the legal domain.
Speaker
Shafi Goldwasser is Director of the Simons Institute for
the Theory of Computing, and Professor of Electrical Engineering
and Computer Science at the University of California Berkeley.
Goldwasser holds a B.S. Applied Mathematics from Carnegie Mellon
University (1979), and M.S. (1981) and Ph.D.(1984) in Computer
Science from the University of California Berkeley.
Goldwasser's pioneering contributions include the introduction of
probabilistic encryption and signatures, zero knowledge
protocols, elliptic curve primality testings, multi-prover
interactive proofs, hardness of approximation proofs for
combinatorial problems, graph property testing, and pseudo
deterministic algorithms and proofs. Goldwasser was the recipient
of the ACM Turing Award in 2012, the Gödel Prize in 1993 and in
2001, the ACM Grace Murray Hopper Award in 1996, the RSA Award in
Mathematics in 1998, the ACM Athena Award for Women in Computer
Science in 2008, the Benjamin Franklin Medal in 2010, the IEEE
Emanuel R. Piore Award in 2011, the Simons Foundation Investigator
Award in 2012, the BBVA Foundation Frontiers of Knowledge Award
in 2018, the L'oreal-Unesco award for Women in Science 2021, and
the FOCS 2021 and STOC 2021 Test of time Awards. Goldwasser is a
member of the NAS, NAE, AAAS, the Russian Academy of Science,
the Israeli Academy of Science, and the London Royal Mathematical
Society. Goldwasser holds honorary degrees from Ben Gurion
University, Bar Ilan University, Carnegie Mellon University,
Haifa University, Tel Aviv University, Oxford University, and
the University of Waterloo, and has received the UC Berkeley
Distinguished Alumnus Award and the Barnard College Medal of Distinction.