As device technologies migrate into Deep Submicron (DSM) feature sizes, high-performance power-efficient computer architectures that keep pace with improving technologies need to be explored. Technology scaling increases the effects of wire latencies, inductive effects, noise and crosstalk in on-chip communication, limiting the performance of DSM designs. Power efficient performance gains from Instruction Level Parallelism (ILP) are reaching a limit. Single-Chip Parallel Computers are promising solutions to the DSM design challenges and the performance limitations of ILP. These systems are explicitly modular architectures that efficiently support Thread Level Parallelism (TLP) while avoiding global signals and shared resources. Microarchitectural level power analysis is required for evaluating the feasibility of newly conceived architectures in terms of power dissipation and energy efficiency. Accounting for power in the early stages of design shortens the time-to-market due to reduced design iteration times. Power optimizations at the architectural level can yield large power savings. This thesis proposes a microarchitectural level power estimation and analysis infrastructure for Single Chip Parallel Computers. The power estimation tool and the analysis methodology are developed based on the Single Chip Message-Passing Parallel (SCMP) Computer and can be extended to other Single Chip Parallel Computers. The thesis focuses on the development of power estimation models, construction of the power analysis tool, study of the power advantages of the architecture and identification of subsystems requiring power optimization. / Master of Science