Tech companies are coming together to set benchmarks for AI – Research Snipers

Tech companies are coming together to set benchmarks for AI


A consortium of 40 tech organizations, including any likes of Facebook and Google, have met up to release a set of assessment benchmarks for AI. By estimating AI items against these benchmarks, organizations in the field will almost certainly recognize ideal item arrangements and, as indicated by the consortium, MLPerf, “take confidence” that they’re conveying the correct solutions.

The benchmarks, named MLPerf Inference v0.5, base on three common machine learning assignments: picture classification, object detection, and machine translation. Given the distinctive handling capacities of various gadgets, there are discrete benchmarks for AI crosswise over different stages, for example, cell phones, servers, and chips.

MLPerf provides benchmark reference implementations that define the problem, model, and quality target, and provide instructions to run the code. The reference implementations are available in ONNX, PyTorch, and TensorFlow frameworks. The MLPerf inference benchmark working group follows an “agile” benchmarking methodology: launching early, involving a broad and open community, and iterating rapidly. The website provides a complete specification with guidelines on the reference code and will track future results.

The inference benchmarks were created thanks to the contributions and leadership of our members over the last 11 months, including representatives from: Arm, Cadence, Centaur Technology, Dividiti, Facebook, General Motors, Google, Habana Labs, Harvard University, Intel, MediaTek, Microsoft, Myrtle, Nvidia, Real World Insights, University of Illinois at Urbana-Champaign, University of Toronto, and Xilinx.

Just as giving best practice direction to organizations in the AI field, it’s trusted that these will help kick-begin further advancement as, in spite of its promotion, associations have been moderate to get the innovation. In a statement, MLPerf’s general chair Peter Mattson said, “By creating common and relevant metrics to assess new machine learning software frameworks, hardware accelerators, and cloud and edge computing platforms in real-life situations, these benchmarks will establish a level playing field that even the smallest companies can use.”

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