Abstract: A scheme to improve performance of power-constrained computers, comprising a heterogeneous mix of compute elements, by dynamically reacting to changes in the switching capacitance that present workload induces in each heterogeneous compute element and learning the coefficients of a power-frequency model for each compute element for the present workload. At each time step, the scheme forecasts a maximum frequency that the compute element can run at without exceeding an input power limit for a given workload. The scheme rapidly re-learns coefficients of the power model and rapidly adapts the frequency as the workload's characteristics shift ensuring that compute elements run at the maximum frequency they can while not exceeding the input power limit.

Abstract: A scheme to improve performance of power-constrained computers, comprising a heterogeneous mix of compute elements, by dynamically reacting to changes in the switching capacitance that present workload induces in each heterogeneous compute element and learning the coefficients of a power-frequency model for each compute element for the present workload. At each time step, the scheme forecasts a maximum frequency that the compute element can run at without exceeding an input power limit for a given workload. The scheme rapidly re-learns coefficients of the power model and rapidly adapts the frequency as the workload's characteristics shift ensuring that compute elements run at the maximum frequency they can while not exceeding the input power limit.