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Abstract

A proactive fault-tolerant scheme which improves performance and energy efficiency for NoCs. The fault-tolerant scheme allows routers to switch among several different fault-tolerant operations. Each operation mode has different trade-offs among fault-tolerant capability, retransmission traffic, latency, and energy efficiency. Another example provides a proactive, dynamic control policy to balance and optimize the dynamic interactions and trade-offs. The example control policy uses example machine learning algorithm called reinforcement learning (RL). The example RL-based controller independently observes a set of NoC system parameters at runtime, and over time they evolve optimal per-router control policies. By automatically and optimally switching among the four fault-tolerant modes, the trained control policy results in minimizing system level network latency and maximizing energy efficiency while detecting and correcting errors.

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First Claim