Abstract
Recent studies on intermittent computing target single-core processors and underestimate the efficient parallel execution of highly-parallelizable machine learning tasks. Even though general-purpose multicore processors provide a high degree of parallelism and programming flexibility, intermittent computing has not exploited them yet. Filling this gap, we introduce AdaMICA (Adaptive Multicore Intermittent Computing) runtime that supports, for the first time, parallel intermittent computing and provides the highest degree of flexibility of programmable general-purpose multiple cores. AdaMICA is adaptive since it responds to the changes in the environmental power availability by dynamically reconfiguring the underlying multicore architecture to use the power most optimally. Our results demonstrate that AdaMICA significantly increases the throughput (52% on average) and decreases the latency (31% on average) by dynamically scaling the underlying architecture, considering the variations in the unpredictable harvested energy.
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