Runtime Monitoring of ML-Based Scheduling Algorithms Toward Robust Domain-Specific SoCs

Machine learning (ML) algorithms are being rapidly adopted to perform dynamic resource management tasks in heterogeneous system on chips. For example, ML-based task schedulers can make quick, high-quality decisions at runtime. Like any ML model, these offline-trained policies depend critically on th...

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Veröffentlicht in:	IEEE transactions on computer-aided design of integrated circuits and systems 2024-11, Vol.43 (11), p.4202-4213
Hauptverfasser:	Goksoy, A. Alper, Kanani, Alish, Chatterjee, Satrajit, Ogras, Umit
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Coherence Data models Decisions Domain-specific system on chip (SoC) Heuristic algorithms imitation learning (IL) Machine learning Measurement Monitoring Real time reinforcement learning (RL) Resource management Resource scheduling Robustness Runtime runtime monitoring System-on-chip Task scheduling Training data Workload
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