Analysis of Model Parallelism for AI Applications on a 64-core RV64 Server CPU

Massive Data Parallel workloads, driven by inference on large ML models, are pushing hardware vendors to develop efficient and cost-effective multi-core server CPUs. The RISC-V architecture plays a prominent role due to its open, extensible, and energy-friendly ISA. Despite significant progress in r...

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Veröffentlicht in:	International journal of parallel programming Jg. 53; H. 4; S. 27
Hauptverfasser:	Malenza, Giulio, Garcia, Adriano Marques, Birke, Robert, Benini, Luca, Aldinucci, Marco
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.08.2025 Springer Nature B.V
Schlagworte:	Artificial intelligence Central processing units Computer Science Computer vision CPUs Design Fashion models Hardware Inference Machine learning Neural networks Open standards Parallel processing Pipelining (computers) Processor Architectures Servers Software Software Engineering/Programming and Operating Systems Theory of Computation Workloads AI RISC-V Model parallelism PyTorch SOPHON SG2042
ISSN:	0885-7458, 1573-7640
Online-Zugang:	Volltext
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