Benchmarking Machine Learning Workloads on Emerging Hardware

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. We have also secured funding from MLCommonsTM to provide a best paper award for an outstanding submission. A panel discussion will foster an interactive platform for discussion between speakers and the audience.