Multi and many-core applications are sensitive to interprocessor communication latencies, suggesting the need for low-latency on-chip networks. We propose a low-latency router architecture that predicts the output channel to be used by the next packet transfer and speculatively completes the switch arbitration to reduce communication latency. The packets coming into the prediction routers are transferred without waiting for the routing computation and switch arbitration if the prediction hits. Thus, the primary concern for reducing communication latency is the hit rates of the prediction algorithms, which vary based on network environments, such as the network topology, routing algorithm, and traffic pattern. Although typical low-latency routers that skip one or more pipeline stages use a bypass data path that is based on a static or single bypassing policy (e.g., accelerating the packets moving in the same dimension), our prediction router architecture predictively forwards packets based on the prediction algorithm selected from among several candidates in response to the network environment. We analyze the prediction hit rates of five prediction algorithms on meshes, tori, fat trees, and Spidergons. Then, we present four case studies, each of which assumes different many-core architectures. We implemented the prediction routers for each case study by using a 45 nm CMOS process, and evaluated them in terms of the prediction hit rate, zero-load latency, hardware amount, and energy consumption. A typical prediction router with two or three predictors shows that although the area and energy are increased by 4.8-12.0 percent and 5.3 percent, respectively, up to 89.8 percent of the prediction hit rate is achieved in real applications, which provides favorable trade-offs between modest hardware/energy overheads and significant latency saving.
- Interconnection networks
- low-latency router architecture.
- on-chip networks
ASJC Scopus subject areas
- Theoretical Computer Science
- Hardware and Architecture
- Computational Theory and Mathematics