Emerging FPGA systems are providing higher external memory bandwidth to compete with GPU performance. However, because FPGAs often achieve parallelism through deep pipelines, traditional FPGA design strategies do not necessarily scale well to large amounts of replicated pipelines that can take advantage of higher bandwidth. We show that sliding-window applications, an important subset of digital signal processing, demonstrate this scalability problem. We introduce a window generator architecture that enables replication to over 330 GB/s, which is an 8.7x improvement over previous work. We evaluate the window generator on the Intel Broadwell+Arria10 system for 2D convolution and show that for traditional convolution (one filter per image), our approach outperforms a 12-core Xeon Broadwell E5 by 81x and a high-end Nvidia P6000 GPU by an order of magnitude for most input sizes, while improving energy by 15.7x. For convolutional neural nets (CNNs), we show that although the GPU and Xeon typically outperform existing FPGA systems, projected performances of the window generator running on FPGAs with sufficient bandwidth can outperform high-end GPUs for many common CNN parameters.

Applications accelerated by field-programmable gate arrays (FPGAs) often require pipelined floating-point accumulators with a variety of different trade-offs. Although previous work has introduced numerous floating-point accumulation architectures, few cores are available for public use, which forces designers to use fixed-point implementations or vendor-provided cores that are not portable and are often not optimized for the desired set of trade-offs. In this article, we combine and extend previous floating-point accumulator architectures into a configurable, open-source core, referred to as the unified accumulator architecture (UAA), which enables designers to choose between different trade-offs for different applications. UAA is portable across FPGAs and allows designers to specialize the underlying adder core to take advantage of device-specific optimizations. By providing an extensible, open-source implementation, we hope for the research community to extend the provided core with new architectures and optimizations.