Self-timed Reinforcement Learning using Tsetlin Machine

Wheeldon, Alan; Yakovlev, Alex; Shafik, Rishad

doi:10.1109/async48570.2021.00014

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Petri nets (PNs) is the formalism that underpins the design and analysis of these examples [1,2,3]. This demonstrates the usefulness of PNs as an interpreted graph model [6,7] for exploring current and future very large concurrent systems.…”

Section: Introductionmentioning

confidence: 93%

“…With the advance of digital computing, there has been a growing demand for modeling very large and complex systems. The research areas of circuit design [1], developmental biology [2], and modeling machine-learning automata [3,4] study systems with millions of interacting elements. At this scale, the models are no longer hand-crafted but automatically synthesized from libraries of components.…”

Section: Introductionmentioning

confidence: 99%

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Practical Distributed Implementation of Very Large Scale Petri Net Simulations

Rafiev

Morris

Xia

et al. 2022

Transactions on Petri Nets and Other Models of Concurrency XVI

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With the continued increase of size and complexity of contemporary digital systems, there is a growing need for models of large size and high complexity, as well as methods of analyzing such models. This paper presents a method for simulating large-scale concurrent Petri net models using parallel distributed hardware platforms. By using POETS architecture, our method allows the mapping of concurrent Petri net executions onto 49,152 parallel processing hardware threads to achieve orders of magnitude (45 to 220 times) improvements of simulation speed, compared to conventional simulation methods using single processor systems. The presented method employs techniques including Petri net model partitioning, the use of max-step and locallyinterleaving semantics, and the fair firing of transitions.

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