Even though they have been around for years, the phrase "MLPerf benchmarks" holds little meaning to most people outside of the AI developer community. However, this community-driven benchmark suite, which measures performance of a broad range of machine learning (ML) tasks, is quickly becoming the gold standard for the fair and unbiased assessment of accelerated computing solutions for machine learning training, inference, and high performance computing (HPC). The era of MLPerf is here, and everyone should be paying attention. Organizations across every industry are racing to take advantage of AI and machine learning to improve their businesses. According to Karl Freund, founder and principal analyst at Cambrian AI Research, businesses should expect that customer demand for AI-accelerated outcomes will continue to grow.

The MLPerf consortium has recently released over 500 validated inference benchmarking results from 14 organizations measuring how fast and how well a pre-trained computer system can classify images, detect objects, and translate sentences. Over 400 of these results were submitted by dividiti, a high-tech company based in Cambridge, UK. "Our success in MLPerf Inference v0.5 is due to our unique open workflow automation technology called Collective Knowledge (CK)", explains Dr Anton Lokhmotov, CEO and co-founder of dividiti. "We conducted literally hundreds of benchmarking experiments, followed by thousands of auditing experiments, with many combinations of machine learning models, libraries, frameworks and hardware platforms. Such experiments are notoriously hard to stage in an automated, portable and reproducible fashion, which explains why even well-resourced hardware vendors only submit a handful of results. In collaboration with Arm and the Polytechnical University of Milan, we staged experiments on systems ranging from Raspberry Pi class boards and Android phones to high-end workstations. Benchmarking anything anywhere is what we call omni-benchmarking."

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 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and four orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf implements a set of rules and practices to ensure comparability across systems with wildly differing architectures. In this paper, we present the method and design principles of the initial MLPerf Inference release. The first call for submissions garnered more than 600 inference-performance measurements from 14 organizations, representing over 30 systems that show a range of capabilities.