US Department of Defense Revamps Major Cloud Procurement

Several years ago, anecdotal evidence led Hyperion Research to compile a list of applications that promised to be the most economically important HPC-enabled AI use cases. Rather than simply drawing attention as interesting one-off examples, these applications had emerged as repetitive AI workloads that vendors could begin to pursue as emerging market segments. Hyperion Research's recently completed multi-client study of the worldwide HPC market presented a direct opportunity to ask HPC user organizations whether they use or plan to use any of the economically important HPC-enabled AI applications.

MLCommons, an international artificial intelligence (AI) standards body formed in 2018, launched MLPerf Tiny, their first benchmark targeted at the inference capabilities of edge and embedded devices, or what they call "intelligence in everyday devices". The new benchmark is now part of the overall MLPerf benchmark suite, which measures AI training and inference performance on a wide variety of workloads, including natural language processing and image recognition. The benchmark covers four machine learning (ML) tasks focused on camera and microphone sensors as inputs: keyword spotting, visual wake words, tiny image classification, and anomaly detection. Some important use cases include smart home security, virtual assistants, and predictive maintenance.