A new technical paper titled “Performance Implications of Multi-Chiplet Neural Processing Units on Autonomous Driving Perception” was publ

Chiplet-Based NPUs to Accelerate Vehicular AI Perception Workloads

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2024-12-23 00:30:09

A new technical paper titled “Performance Implications of Multi-Chiplet Neural Processing Units on Autonomous Driving Perception” was published by researchers at UC Irvine.

Abstract “We study the application of emerging chiplet-based Neural Processing Units to accelerate vehicular AI perception workloads in constrained automotive settings. The motivation stems from how chiplets technology is becoming integral to emerging vehicular architectures, providing a cost-effective trade-off between performance, modularity, and customization; and from perception models being the most computationally demanding workloads in a autonomous driving system. Using the Tesla Autopilot perception pipeline as a case study, we first breakdown its constituent models and profile their performance on different chiplet accelerators. From the insights, we propose a novel scheduling strategy to efficiently deploy perception workloads on multi-chip AI accelerators. Our experiments using a standard DNN performance simulator, MAESTRO, show our approach realizes 82% and 2.8x increase in throughput and processing engines utilization compared to monolithic accelerator designs.”

Odema, Mohanad, Luke Chen, Hyoukjun Kwon, and Mohammad Abdullah Al Faruque. “Performance Implications of Multi-Chiplet Neural Processing Units on Autonomous Driving Perception.” arXiv preprint arXiv:2411.16007 (2024).

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