Approximate Pattern Matching for On-Chip Interconnect Traffic Prediction

Emerging multi-chip module GPUs (MCM-GPUs) expend over 17% of the total power budget on chip interconnects and this fraction is expected to increase as chip size increases. Towards proactively managing the power consumption of these interconnects, we propose approximate pattern matching to predict future interconnect traffic from past observations. Compared to past prediction techniques such as Markov model (MM) and history table (HT), our proposed technique reduces average prediction error to 2.66% from 7.11% and 3.83% for MM and HT, respectively.