BandiTS: Dynamic timing speculation using multi-armed bandit based optimization
Jeff Jun Zhang, Siddharth Garg · 2017
Timing speculation has recently been proposed as a method for increasing performance beyond that achievable by conventional worst-case design techniques. Starting with the observation of fast temporal variations in timing error probabilities, we propose a run-time technique to dynamically determine the optimal degree of timing speculation (i.e., how aggressively the processor is over-clocked) based on a novel formulation of the dynamic timing speculation problem as a multi-armed bandit problem. By conducting detailed post-synthesis timing simulations on a 5-stage MIPS processor running a variety of workloads, the proposed adaptive mechanism improves processor's performance significantly comparing with a competing approach (about 8.3% improvement); on the other hand, it shows only about 2.8% performance loss on average, compared with the oracle results.