General-purpose GPUs (GPGPUs) are becoming prevalent in mainstream computing, and performance per watt has emerged as a more crucial evaluation metric than peak performance. As such, GPU architects require robust tools that will enable them to quickly explore new ways to optimize GPGPUs for energy efficiency. We propose a new GPGPU power model that is configurable, capable of cycle-level calculations, and carefully validated against real hardware measurements. To achieve configurability, we use a bottom-up methodology and abstract parameters from the microarchitectural components as the model's inputs. We developed a rigorous suite of 80 microbenchmarks that we use to bound any modeling uncertainties and inaccuracies. The power model is comprehensively validated against measurements of two commercially available GPUs, and the measured error is within 9.9% and 13.4% for the two target GPUs (GTX 480 and Quadro FX5600). The model also accurately tracks the power consumption trend over time. We integrated the power model with the cycle-level simulator GPGPU-Sim and demonstrate the energy savings by utilizing dynamic voltage and frequency scaling (DVFS) and clock gating. Traditional DVFS reduces GPU energy consumption by 14.4% by leveraging within-kernel runtime variations. More finer-grained SM cluster-level DVFS improves the energy savings from 6.6% to 13.6% for those benchmarks that show clustered execution behavior. We also show that clock gating inactive lanes during divergence reduces dynamic power by 11.2%.

All practical wireless communication systems are prone to errors. At the symbol level such wireless errors have a well-defined structure: when a receiver decodes a symbol erroneously, it is more likely that the decoded symbol is a good "approximation" of the transmitted symbol than a randomly chosen symbol among all possible transmitted symbols. Based on this property, we define approximate communication, a method that exploits this error structure to natively provide unequal error protection to data bits. Unlike traditional (FEC-based) mechanisms of unequal error protection that consumes additional network and spectrum resources to encode redundant data, the approximate communication technique achieves this property at the PHY layer without consuming any additional network or spectrum resources (apart from a minimal signaling overhead) . Approximate communication is particularly useful to media delivery applications that can benefit significantly from unequal error protection of data bits. We show the usefulness of this method to such applications by designing and implementing an end-to-end media delivery system, called Apex. Our Software Defined Radio (SDR)-based experiments reveal that Apex can improve video quality by 5 to 20 dB (PSNR) across a diverse set of wireless conditions, when compared to traditional approaches. We believe that mechanisms such as Apex can be a cornerstone in designing future wireless media delivery systems under any errorprone channel condition.

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Researchers in embedded and reconfigurable computing are often hindered by a lack of suitable benchmarks with which to accurately evaluate their work. Without a suitable benchmark suite, researchers use either outdated, unrealistic benchmarks or spend valuable time creating their own. In this paper, we present ERCBench-a freely-available, open-source benchmark suite geared towards embedded and reconfigurable computing research. ERCBench benchmarks represent a variety of application areas, including multimedia processing, wireless communications, and cryptography. They consist of synthesizable Verilog models for hardware accelerators and hybrid hardware/software applications that combine softwarebased control flow with hardware-based computation tasks.

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