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Artificial neural networks based dynamic priority arbitration for asynchronous flow control

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Abstract

Accesses to physical links in Networks-on-Chip need to be appropriately arbitrated to avoid collisions. In the case of asynchronous routers, this arbitration between various clients, carrying messages with different service levels, is managed by dedicated circuits called arbiters. The latter are accustomed to allocate the shared resource to each client in a round-robin fashion; however, they may be tuned to favor certain messages more frequently by means of various digital design techniques. In this work, we make use of artificial neural networks to propose a mechanism to dynamically compute priority for each message by defining a few constraints. Based on these constraints, we first build a mathematical model for the objective function, and propose two algorithms for vector selection and resource allocation to train the artificial neural networks. We carry out a detailed comparison between seven different learning algorithms, and observe their effectiveness in terms of prediction efficiency for the application of dynamic priority arbitration. The decision is based on input parameters: available tokens, service levels, and an active request from each client. The performance of the learning algorithms has been analyzed in terms of mean squared error, true acceptance rate, number of epochs and execution time, so as to ensure mutual exclusion.

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Notes

  1. Our particular application does not need run-time training, since our architecture, the number of inputs and outputs, number of virtual channels (i.e., clients) usually will all remain fixed during the operation—so offline training once will be sufficient.

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Authors and Affiliations

  1. Department of Electrical Engineering, COMSATS Institute of Information Technology (CIIT), Wah Cantonment, Pakistan

    Syed Rameez Naqvi, Tallha Akram, Sajjad Ali Haider & Muhammad Kamran

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  1. Syed Rameez Naqvi
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Naqvi, S.R., Akram, T., Haider, S.A. et al. Artificial neural networks based dynamic priority arbitration for asynchronous flow control. Neural Comput & Applic 29, 627–637 (2018). https://doi.org/10.1007/s00521-016-2571-6

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