On Performance Optimization and Quality Control for Approximate-communication-enabled Networks-on-Chip

For many applications showing error forgiveness, approximate computing is a new design paradigm that trades application output accuracy for mitigating computation/communication effort, which results in performance/energy benefit. Since networks-on-chip (NoCs) are one of the major contributors to system performance and power consumption, the underlying communication is approximated to achieve time/energy improvement. However, performing approximation blindly causes unacceptable quality loss. In this paper, first, an optimization problem to maximize NoC performance is formulated with the constraint of application quality requirement, and the application quality loss is studied. Second, a congestion-aware quality control method is proposed to improve system performance by aggressively dropping network data, which is based on flow prediction and a lightweight heuristic. In the experiments, two recent approximation methods for NoCs are augmented with our proposed control method to compare with their original ones. Experimental results show that our proposed method can speed up execution by as much as 29.42% over the two state-of-the-art works.