Power models supporting energy-efficient co-design on ultra-low power embedded systems

The energy efficiency of computing systems can be enhanced via power models that provide insights into how the systems consume power. However, there are no application-general, fine-grained and validated power models which can provide insights into how a given application running on an ultra-low power (ULP) embedded system consumes power. In this study, we devise new fine-grained power models that provide insights into how a given application consumes power on an ULP embedded system. The models support architecture-application co-design by considering both platform and application properties. The models are validated with data from 35 micro-benchmarks and three application kernels, namely dense matrix multiplication, sparse matrix vector multiplication and breadth first search, on Movidius Myriad, an ultra-low power embedded system. The absolute percentage errors of the model are at most 8.5% for micro-benchmarks and 12% for application kernels. Based on the models, we propose a framework predicting when to apply race-to-halt (RTH) strategy (i.e., running an application with a maximum setting) to a given application. For the three validated application kernels, the proposed framework is able to predict when to use RTH and when not to use RTH precisely. The experimental results show that the prediction of our new RTH framework is accurate and we can save up to 61% energy for dense matrix multiplication, 59% energy for sparse matrix vector multiplication by using RTH and 5% energy for breadth first search by not using RTH.