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? Once approved, go to the Automotive Benchmark Task Force folder in our Public Google Drive.Ask to join our Public MLCommons Google Group.Associate a Google account with your corporate e-mail address.Requests are manually reviewed, so please be patient.Īccess shared documents and meeting minutes: Request to join the Automotive Google Group to sign up for the mailing list and receive the weekly meeting invite. How to Join the Automotive Benchmark Task Force Industry driven alliance keep us ahead of regulatory requirements. Regulatory Compliance - Automotive regulation is very strict and collaboration is key to meet regulations around the use of AI/ML in vehicles, particularly in relation to safety.User Experience - Consumers have a better experience in a vehicle with smart function.Efficiency - AI/ML is used to optimize vehicle performance, including fuel efficiency and predictive maintenance.Measuring AI performance ensures automotive systems to meet specific latency and other performance KPIs. Safety - AI/ML enhances safety features such as collision avoidance, lane departure warning and other intelligent driving features.Why is AI/ML Important for Automotive designs? This is a crucial step when assessing whether the sourced part is a suitable choice when designing the next-generation automotive compute platforms. OEMs and automotive suppliers regularly send out RFIs and RFQs to vendors to understand a solution’s compute performance and system resource utilization. Define and develop an industry standard ML benchmark suite for automotive to be used in request for information (RFIs) and request for quotation (RFQs).
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