Design Space Exploration of 3D Network-on-Chip: A Sensitivity-based Optimization Approach

ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS

Article

Lee, Dongjin¹  Das, Sourav¹  Kim, Dae Hyun¹  Doppa, Janardhan Rao¹  Pande, Partha Pratim¹ 

[1] Washington State Univ, Sch Elect Engn & Comp Sci, Pullman, WA 99163 USA.

关键词: 3D NoC;    link placement optimization;    small-world network;    sensitivity;    SMALL-WORLD;    NOC;   

DOI  :  10.1145/3197567

来源: SCIE

【 摘 要 】

High-performance and energy-efficient Network-on-Chip (NoC) architecture is one of the crucial components of the manycore processing platforms. A very promising NoC architecture recently proposed in the literature is the three-dimensional small-world NoC (3D SWNoC). Due to short vertical links in 3D integration and the robustness of small-world networks, the 3D SWNoC architecture outperforms its other 3D counterparts. However, the performance of 3D SWNoC is highly dependent on the placement of the links and associated routers. In this article, we propose a sensitivity-based link placement algorithm (SEN) to optimize the performance of 3D SWNoC. The sensitivity of a link in a NoC measures the importance of the link. The SEN algorithm optimizes the performance of 3D SWNoC by calculating the sensitivities of all the links in the NoC and removing the least important link repeatedly. We compare the performance of SEN algorithm with simulated annealing- (SA) and recently proposed machine-learning-based (ML) optimization algorithm. The optimized 3D SWNoC obtained by the proposed SEN algorithm achieves, on average, 11.5% and 13.6% lower latency and 18.4% and 21.7% lower energy-delay product than those optimized by the SA and ML algorithms respectively. In addition, the SEN algorithm is 26 to 33 times faster than the SA algorithm for the optimization of 64-, 128-, and 256-core 3D SWNoC designs. The performance gain provided by the SEN-, SA-, and ML-based methods also depend on the characteristics of the benchmarks under consideration. If the traffic pattern generated by a benchmark does not have enough variation, then the ML-based method does not have adequate opportunity to optimize the network. However, we find that ML-based methodology has faster convergence time than SEN and SA for bigger systems. The ML-based optimization algorithm is almost 4 and 97 times faster than the SEN- and SA-based algorithm for a system with 256 cores.

【 授权许可】

Free