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.”
Find the technical paper here. Preprint November 2024.
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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