High power consumption during test may lead to yield loss and premature aging. In particular, excessive peak power consumption during at-speed delay fault testing represents an important issue. In the literature, several techniques have been proposed to reduce peak power consumption during at-speed LOC or LOS delay testing. On the other side, limiting too much the power consumption during test may reduce the defect coverage. Hence, techniques for identifying upper and lower functional power limits are crucial for delay fault testing. Yet, the task of computing the maximum functional peak power achievable by CPU cores is challenging, since the functional patterns with maximum peak power depend on specific instruction execution order and operands. In this paper, we present a methodology combining neural networks and evolutionary computing for quickly estimating peak power consumption. The method is used within an algorithm for automatic functional program generation used to identify test programs with maximal functional peak power consumption, which are suitable for defining peak power limits under test. The proposed approach was applied on the Intel 8051 CPU core synthesized with a 65 nm industrial technology reducing significant time with respect to old methods.