Abstract Machine learning is experiencing an explosion of software and hardware solutions,
and needs industry-standard performance benchmarks to drive design and enable …

In this article, we describe the design choices behind MLPerf, a machine learning
performance benchmark that has become an industry standard. The first two rounds of the …

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML
applications, the number of different ML inference systems has exploded. Over 100 …

As the computational requirements for machine learning systems and the size and
complexity of machine learning frameworks increases, essential framework innovation has …

Large deep neural network models have recently demonstrated state-of-the-art accuracy on
hard visual recognition tasks. Unfortunately such models are extremely time consuming to …

Strong empirical evidence that one machine-learning algorithm A outperforms another one
B, ideally calls for multiple trials optimizing the learning pipeline over sources of variation …

In this paper we argue that systems for numerical computing are stuck in a local basin of
performance and programmability. Systems researchers are doing an excellent job …

There is an increasing need to bring machine learning to a wide diversity of hardware
devices. Current frameworks rely on vendor-specific operator libraries and optimize for a …

Deep neural networks (DNNs) have gained tremendous attractions as compelling solutions
for applications such as image classification, object detection, speech recognition, and so …

While deep learning is an exciting new technique, the benefits of this method need to be
assessed with respect to its computational cost. This is particularly important for deep …