3–6 Oct 2022
Southern Methodist University
America/Chicago timezone

FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning

5 Oct 2022, 14:15
15m
Southern Methodist University

Southern Methodist University

Speaker

Jules Muhizi (Fermilab/Harvard University)

Description

Applications of machine learning (ML) are growing by the day for many unique
and challenging scientific applications. However, a crucial challenge facing
these applications is their need for ultra low-latency and on-detector ML
capabilities. Given the slowdown in Moore's law and Dennard scaling, coupled
with the rapid advances in scientific instrumentation that is resulting in
growing data rates, there is a need for ultra-fast ML at the extreme edge. Fast
ML at the edge is essential for reducing and filtering scientific data in
real-time to accelerate science experimentation and enable more profound
insights. To accelerate real-time scientific edge ML hardware and software
solutions, we need well-constrained benchmark tasks with enough specifications
to be generically applicable and accessible. These benchmarks can guide the
design of future edge ML hardware for scientific applications capable of
meeting the nanosecond and microsecond level latency requirements. To this end,
we present an initial set of scientific ML benchmarks, covering a variety of ML
and embedded system techniques.

Primary authors

Benjamin Hawks (Fermi National Accelerator Lab) Christian Herwig (Fermi National Accelerator Lab. (US)) Javier Mauricio Duarte (Univ. of California San Diego (US)) Jules Muhizi (Fermilab/Harvard University) Nhan Tran (Fermi National Accelerator Lab. (US)) Shvetank Prakash (Harvard University) Vijay Janapa Reddi (Harvard University)

Presentation materials