  Future multiprocessor chips will integrate many different units, each
tailored to a specific computation. When designing such a system, the chip
architect must decide how to distribute limited system resources such as area,
power, and energy among the computational units. We extend MultiAmdahl, an
analytical optimization technique for resource allocation in heterogeneous
architectures, for energy optimality under a variety of constant system power
scenarios. We conclude that reduction in constant system power should be met by
reallocating resources from general-purpose computing to heterogeneous
accelerator-dominated computing, to keep the overall energy consumption at a
minimum. We extend this conclusion to offer an intuition regarding
energy-optimal resource allocation in data center computing.
