Year: 2022
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Duarte, Javier, Tran, Nhan, Hawks, Ben, Herwig, Christian, Muhizi, Jules, Prakash, Shvetank, Reddi, Vijay Janapa
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Collaborative Proposal: Geodynamic Tomography: A New Approach to Studies of Mantle Upwelling and its Application to the Lau Basin and The East Pacific Rise, G2926102538, G3270322552, G4404904460, G498139845, FAIR FRAMEWORK FOR PHYSICS-INSPIRED AI IN HIGH ENERGY PHYSICS, G5616713406, G585908603, HDR Institute: Accelerated AI Algorithms for Data-Driven Discovery, G716412294, REAL-TIME ARTIFICIAL INTELLIGENCE FOR PARTICLE RECONSTRUCTION AND HIGGS PHYSICS, REAL-TIME ARTIFICIAL INTELLIGENCE FOR PARTICLE RECONSTRUCTION AND HIGGS PHYSICS, G8623004658, G969889393
OpenAlex ID: W4286233963