FastML Science Benchmarks: Accelerating Scientific Edge ML [Poster]
Javier Mauricio Duarte, Nhan Viet Tran, Benjamin Hawks, T. C. Herwig, Jules Muhizi, S. Prakash, Vijay Janapa Reddi · 2022
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.