Evaluating and Analyzing Machine Learning Workloads on Contemporary Processors

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Machine Learning workloads are becoming increasingly more prevalent and compute-intensive. They are run on standard multicore porocessors and accelerators such as GPUs, as well as custom or semi-custom devices such as  Tensor Processing Unnits and  Qualcomms  Snapdragon DSP core.

This project will involve the benchmarking and performance analysis of various ML, with an emphasis on Deep Learning, workloadws, on a slection of processors, including standard x86-64 processors, GPUs and custom devices. The goals will be to find what are the dominant functions in the workloads andtheir characteristics (e.g. memory vs compute intensity), and to evaluate the effectivness of the different classes of architectures on processing them.

References

Brandon Reagen,et al, Deep Learning for Computer Architects, Morgan & Claypool, 2017, http://www.morganclaypoolpublishers.com/catalog_Or...

Deep Learning Benchmark, https://github.com/u39kun/deep-learning-benchmark

AI accelerator, https://en.wikipedia.org/wiki/AI_accelerator

 

 

Updated:  15 May 2018/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing