
MLCommons Adds Edge/Embedded AI Inference Benchmark
$1,500.00
Authors: Alex Norton and Bob Sorensen
Publication Date: 6 202021
Length: 1 pages
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.
Related Products
US Government Proposed FY 2022 Budget Targets Increased Funding to Support Domestic Quantum Information Science
Bob Sorensen, Tom Sorensen
The US Office of Science and Technology Policy recently released its second annual National Quantum Initiative (NQI) report, a supplement to the President's FY22 Budget Request that outlines the major US government quantum information science (QIS) research activities and related funding levels out to FY 2022. As seen in Figure 1, the proposed FY2022 budget, which is targeted for about $880 million, calls for an increase of nearly 11% from the previous year. Roughly half of the funding is to come from the NQI and the other half from base agency-specific QIS R&D budgets. The figure represents the sum of Federal budgets for U.S. QIS R&D efforts in over a dozen agencies including NIST, NSF, DOE, NASA, DOD, and DHS, and it also aggregates several QIS subtopics such as computing, networking, sensing, fundamental science, and end quantum-related use cases
December 202021 | HYP_Link
AI-Centered Partnership Between OMRON and Kyoto University Targets Cardiovascular Diseases
Tom Sorensen, Alex Norton
At the recent CES 2022 tech event, Japanese corporation OMRON, the world’s leading manufacturer and distributor of personal heart health products and other medical devices, highlighted (and later announced on their website) the activities of their partnership with Kyoto University to develop an AI[1]powered platform that uses remotely gathered patient data to predict cardiovascular diseases at an earlier stage than current averages. Kyoto University is closely tied to the identity of Japan's government and considered Japan's leading research university. They operate a Top500 HPC system on-site in addition to conducting research on the cutting edge Fugaku supercomputer. This new program, part of an ongoing partnership between the two organizations, seeks to explore the use of AI to analyze blood pressure metrics for early detection of cardiovascular diseases faster and with greater accuracy allowing for treatment courses to be changed or taken more quickly.
1 202022 | HYP_Link

