Abstract
<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Machine learning (ML)</b> is adopted into a wide range of applications. The popularity and adoption came because of performance and capability to deploy in real-world systems and the ease of use. Given a wide range of works proposed on ML, ranging from the ML learning methodology to the design of hardware accelerators, different works focus on various aspects, depending on the application requirements. For a fair comparison and benefit of the society, benchmarking of the ML applications and performance is nontrivial. Such a benchmarking will also enable the community to analyze and fairly compare the solutions.
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