Comparing the performance of FPGA-based custom computers with general-purpose computers for DSP applications

When FPGA logic circuits are incorporated within a stored-program computer, the result is a machine where the programmer can design both the software and the hardware that will execute that software. This paper first describes some of the more important custom computers, and their potential weakness as DSP implementation platforms. It then describes a new custom computing architecture which is specifically designed for efficient implementation of DSP algorithms. Finally, it presents a simple performance comparison of a number of DSP implementation alternatives, and concludes that the new custom computing architecture is worthy of further investigation, and that custom computers based only on FPGA execution units show little performance improvement over state-of-the-art workstations

High throughput spatial convolution filters on FPGAs

Digital signal processing (DSP) on field- programmable gate arrays (FPGAs) has long been appealing because of the inherent parallelism in these computations that can be easily exploited to accelerate such algorithms. FPGAs have evolved significantly to further enhance the mapping of these algorithms, included additional hard blocks, such as the DSP blocks found in modern FPGAs. Although these DSP blocks can offer more efficient mapping of DSP computations, they are primarily designed for 1-D filter structures. We present a study on spatial convolutional filter implementations on FPGAs, optimizing around the structure of the DSP blocks to offer high throughput while maintaining the coefficient flexibility that other published architectures usually sacrifice. We show that it is possible to implement large filters for large 4K resolution image frames at frame rates of 30–60 FPS, while maintaining functional flexibility