Due to the advancement of transistor technology, a single chip processor can now have hundreds of cores. Network-on-Chip (NoC) has been the superior interconnect fabric for multi/many-core on-chip systems because of its scalability and parallelism. Due to the rise of dark silicon with the end of Dennard Scaling, it becomes essential to design energy efficient and high performance heterogeneous NoC-based multi/many-core architectures. Because of the large and complex design space, the solution space becomes difficult to explore within a reasonable time for optimal trade-offs of energy-performance-reliability. Furthermore, reactive resource management is not effective in preventing problems from happening in adaptive systems. Therefore, in this work, we explore machine learning techniques to design and configure the NoC resources based on the learning of the system and applications workloads. Machine learning can automatically learn from past experiences and guide the NoC intelligently to achieve its objective on performance, power, and reliability. We present the challenges of NoC design and resource management and propose a generalized machine learning framework to uncover near-optimal solutions quickly. We propose and implement a NoC design and optimization solution enabled by neural networks, using the generalized machine learning framework. Simulation results demonstrated that the proposed neural networks-based design and optimization solution improves performance by 15% and reduces energy consumption by 6% compared to an existing non-machine learning-based solution while the proposed solution improves NoC latency and throughput compared to two existing machine learning-based NoC optimization solutions. The challenges of machine learning technique adaptation in multi/many-core NoC have been presented to guide future research.

由于晶体管技术的进步，单芯片处理器现在可以拥有数百个核心。片上网络 (NoC)由于其可扩展性和并行性，它一直是多核/众核片上系统的卓越互连结构。由于暗硅的兴起以及 Dennard Scaling 的终结，设计节能且高性能的基于 NoC 的异构多核/众核架构变得至关重要。由于设计空间庞大且复杂，因此很难在合理的时间内探索解决方案空间以实现能源-性能-可靠性的最佳权衡。此外，反应性资源管理不能有效地防止自适应系统中出现问题。因此，在这项工作中，我们探索机器学习技术，根据系统和应用程序工作负载的学习来设计和配置 NoC 资源。机器学习可以自动从过去的经验中学习，并智能地指导 NoC 实现其性能、功耗和可靠性目标。我们提出了 NoC 设计和资源管理的挑战，并提出了一个通用的机器学习框架，以快速发现接近最优的解决方案。我们使用通用机器学习框架提出并实施由神经网络支持的片上网络设计和优化解决方案。仿真结果表明，与现有的基于非机器学习的解决方案相比，所提出的基于神经网络的设计和优化解决方案将性能提高了 15%，能耗降低了 6%，同时与现有的两种解决方案相比，所提出的解决方案提高了 NoC 延迟和吞吐量。基于机器学习的NoC优化解决方案。