Adaptive Mitigation of Parameter Variations

In the deep nanoscale regime, process and runtime variations have emerged as the major sources of uncertainty and unpredictability in circuit operation. Static mitigation approaches do not consider the dependence of variations on workload and chip usage, while adaptive techniques do not incorporate detailed circuit-level information. We propose a fine-grained adaptive technique in which machine learning is exploited to perform circuit clustering and obtain a representative for each cluster. By monitoring the representative in each cluster at runtime, performance variations in the entire cluster can be tracked such that appropriate fine-grained adaptation can be applied to each cluster. Experimental results for ISCAS'89, IWLS'05, and ITC'99 benchmarks as well as the LEON processor show that the proposed approach introduces negligible overhead significantly extends circuit lifetime, facilitates higher operating frequencies, and reduces the leakage power.