  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.
