Slim NoC: A Low-Diameter On-Chip Network Topology for High Energy Efficiency and Scalability
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Abstract (arXiv)
Emerging chips with hundreds and thousands of cores require networks with unprecedented energy/area efficiency and scalability. To address this, we propose Slim NoC (SN): a new on-chip network design that delivers significant improvements in efficiency and scalability compared to the state-of-the-art. The key idea is to use two concepts from graph and number theory, degree-diameter graphs combined with non-prime finite fields, to enable the smallest number of ports for a given core count. SN is inspired by state-of-the-art off-chip topologies; it identifies and distills their advantages for NoC settings while solving several key issues that lead to significant overheads on-chip. SN provides NoC-specific layouts, which further enhance area/energy efficiency. We show how to augment SN with state-of-the-art router microarchitecture schemes such as Elastic Links, to make the network even more scalable and efficient. Our extensive experimental evaluations show that SN outperforms both traditional low-radix topologies (e.g., meshes and tori) and modern high-radix networks (e.g., various Flattened Butterflies) in area, latency, throughput, and static/dynamic power consumption for both synthetic and real workloads. SN provides a promising direction in scalable and energy-efficient NoC topologies.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 2.
5 papers.
- 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
- 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
- 2021GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebracited by 10
- 2024Demystifying Higher-Order Graph Neural Networkscited by 4
- 2022Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load Predictioncited by 3
- 2024Demystifying Higher-Order Graph Neural Networks18 Jun 2024
- 2024Demystifying Chains, Trees, and Graphs of Thoughts25 Jan 2024
- 2022Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load Prediction1 Sep 2022
- 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis19 May 2022
- 2021GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra5 Mar 2021