With evolving system architectures, hardware and software stack, diverse machine learning workloads,
and data, it is important to understand how these components interact with each other. Well-defined
benchmarking procedures help evaluate and reason the performance gains with ML workload to system
mapping.
Key problems that we seek to address are: (i) which representative ML benchmarks cater to workloads
seen in industry, national labs, and interdisciplinary sciences; (ii) how to characterize the ML workloads
based on their interaction with hardware; (iii) what novel aspects of hardware, such as heterogeneity in
compute, memory, and bandwidth, will drive their adoption; (iv) performance modeling and projections
to next-generation hardware.
The workshop will invite experts in these research areas to present recent work and potential directions
to pursue. Accepted papers from a rigorous evaluation process will present state-of-the-art research
efforts. A panel discussion will foster an interactive platform for discussion between speakers and the
audience.