The increasing usage of hardware accelerators such as Field-Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) has significantly increased application design complexity. Such complexity results from a larger design space created by numerous combinations of accelerators, algorithms, and hw/sw partitions. Exploration of this increased design space is critical due to widely varying performance and energy consumption for each accelerator when used for different application domains and different use cases. To address this problem, numerous studies have evaluated specific applications across different architectures. In this article, we analyze an important domain of applications, referred to as sliding-window applications , implemented on FPGAs, GPUs, and multicore CPUs. For each device, we present optimization strategies and analyze use cases where each device is most effective. The results show that, for large input sizes, FPGAs can achieve speedups of up to 5.6× and 58× compared to GPUs and multicore CPUs, respectively, while also using up to an order of magnitude less energy. For small input sizes and applications with frequency-domain algorithms, GPUs generally provide the best performance and energy.

Recent architectural trends have focused on increased parallelism via multicore processors and increased heterogeneity via accelerator devices (e.g., graphics-processing units, field-programmable gate arrays). Although these architectures have significant performance and energy potential, application designers face many device-specific challenges when choosing an appropriate accelerator or when customizing an algorithm for an accelerator. To help address this problem, in this article we thoroughly evaluate convolution, one of the most common operations in digital-signal processing, on multicores, graphics-processing units, and field-programmable gate arrays. Whereas many previous application studies evaluate a specific usage of an application, this article assists designers with design space exploration for numerous use cases by analyzing effects of different input sizes, different algorithms, and different devices, while also determining Pareto-optimal trade-offs between performance and energy.

With the emergence of accelerator devices such as multicores, graphics-processing units (GPUs), and field-programmable gate arrays (FPGAs), application designers are confronted with the problem of searching a huge design space that has been shown to have widely varying performance and energy metrics for different accelerators, different application domains, and different use cases. To address this problem, numerous studies have evaluated specific applications across different accelerators. In this paper, we analyze an important domain of applications, referred to as sliding-window applications, when executing on FPGAs, GPUs, and multicores. For each device, we present optimization strategies and analyze use cases where each device is most effective. The results show that FPGAs can achieve speedup of up to 11x and 57x compared to GPUs and multicores, respectively, while also using orders of magnitude less energy.