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The rapid advancements in the computational capabilities of the graphics processing unit (GPU) as well as the deployment of general programming models for these devices have made the vision of a desktop supercomputer a reality. It is now possible to assemble a system that provides several TFLOPs of performance on scientific applications for the cost of a high-end laptop computer. While these devices have clearly changed the landscape of computing, there are two central problems that arise. First, GPUs are designed and optimized for graphics applications resulting in delivered performance that is far below peak for more general scientific and mathematical applications. Second, GPUs are power hungry devices that often consume 100-300 watts, which restricts the scalability of the solution and requires expensive cooling. To combat these challenges, this paper presents the PEPSC architecture-an architecture customized for the domain of data parallel dense matrix style scientific application where power efficiency is the central focus. PEPSC utilizes a combination of a 2D single-instruction multiple-data (SIMD) datapath, an intelligent dynamic prefetching mechanism, and a configurable SIMD control approach to increase execution efficiency over conventional GPUs. A single PEPSC core has a peak performance of 120 GFLOPs while consuming 2 W of power when executing modern scientific applications, which represents an increase in computation efficiency of more than 10X over existing GPUs.