![]() ![]() Weekly on Wednesday from 8:05-9:00AM Pacific. With time, the benchmark suite will be updated to take these new use-cases in consideration. However, the use of ML in new areas is increasing as more features adopt more sophisticated algorithms. The focus will be on, but not limited to, camera sensor perception as this is currently the most mature type of ML for automotive. The WG will create a MLPerf benchmark suite for automotive running on systems developed for automotive purposes. The following diagram illustrates the high-level LoadGen control concept. The ABTF benchmark will leverage the existing MLCommons LoadGen infrastructure running on dedicated AI/ML automotive hardware. The following figure illustrates how the ABTF is focused strictly on PERCEPTION processing for the first benchmark demonstration. ![]() Measuring automotive AI/ML performance requires focus to specific areas in the image processing chain. These benchmarks will help drive the industry forwards faster. The benefit of having an industy ML automotive benchmark suite is two-fold: it makes it easier to do fair (so called apples-to-apples) comparisons between different technologies, and help guide where to focus engineering efforts for developing future hardware and optimizing current software. The Automotive Benchmark Task Force (ABTF) is focused on defining the right AI/ML performance targets and specifications to ensure fairness, accuracy and broad industry adoption.Inaccurate AI/ML performance measurements increase financial, technical and safety risk.AVCC has a proven track record of publishing Technical Reports on how to measurement ML performance for automotive.MLCommons has a proven track record of developing well adopted industry standard ML benchmark suites Edge, Datacenter and Mobile.Why are MLCommons and AVCC Partnering on Automotive AI/ML Performance? ![]()
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