IEEE Access (Jan 2025)

DA+BMAC: Distance-Aware Bidirectional Medium Access Control for Mesh Wireless Network-on-Chip

  • Mohd Shahrizal Rusli,
  • Asrani Lit,
  • Muhammad Nadzir Marsono,
  • Ab Al-Hadi Ab Rahman,
  • Shahidatul Sadiah,
  • Michael Loong Peng Tan,
  • Suhaila Isaak,
  • Norlina Paraman

DOI
https://doi.org/10.1109/access.2025.3594190
Journal volume & issue
Vol. 13
pp. 140914 – 140932

Abstract

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Medium access control (MAC) protocol and routing strategy significantly influence Wireless Network-on-Chip (WiNoC) performance and energy characteristics. Conventional WiNoC MAC typically utilizes a daisy-chained ring topology, which limits wireless channel performance due to maximum waiting times when a radio hub misses the token before packet arrival. Additionally, due to limited wireless channel bandwidth compared to aggregate wired interconnect capacity, radio hubs become susceptible to congestion when multiple processing cores simultaneously request wireless access. The lack of intelligent traffic control leads to indiscriminate wireless transmission even when efficient wired paths are available. This paper proposes DA+BMAC, a Distance-Aware (DA) Bidirectional Medium Access Control (BMAC) scheme for mesh WiNoC architecture. BMAC employs bidirectional links allowing token reversal when it overshoots an idle radio hub, reducing worst-case token wait time. DA+BMAC implements distance-aware routing to reserve single-hop wireless transmission exclusively for source-destination pairs beyond a specific distance threshold, while dedicating wired interconnects with higher aggregate bandwidth to short-range communication. Comprehensive validation using a cycle-accurate Noxim simulator on an $8\times 8$ mesh with 16 radio hubs demonstrates up to $11.49\times $ throughput improvement and 15% energy savings compared to a baseline token-ring WiNoC. Scalability analysis confirms performance benefits extend to larger networks (256 and 1024 cores), making DA+BMAC suitable for future many-core systems. The proposed approach has been validated using both synthetic traffic distributions (random, shuffle, transpose, and hotspot) and real-application traces (Barnes and Fluidanimate) from PARSEC and SPLASH-2 benchmark suites, confirming its effectiveness across diverse workloads.

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