The energy in FPGA computations is dominated by data communication energy, either in the form of memory references or data movement on interconnect. In this article, we explore how to use data placement and parallelism to reduce communication energy. We show that parallelism can reduce energy and that the optimal level of parallelism increases with the problem size. We further explore how FPGA memory architecture (memory block size(s), memory banking, and spacing between memory banks) can impact communication energy, and determine how to organize the memory architecture to guarantee that the energy overhead compared to the optimally matched architecture for the design is never more than 60%. We specifically show that an architecture with 32 bit wide, 16Kb internally banked memories placed every 8 columns of 10 4-LUT logic blocks is within 61% of the optimally matched architecture across the VTR 7 benchmark set and a set of parallelism-tunable benchmarks. Without internal banking, the worst-case overhead is 98%, achieved with an architecture with 32 bit wide, 8Kb memories placed every 9 columns, roughly comparable to the memory organization on the Cyclone V (where memories are placed about every 10 columns). Monolithic 32 bit wide, 16Kb memories placed every 10 columns (comparable to 18Kb and 20Kb memories used in Virtex 4 and Stratix V FPGAs) have a 180% worst-case energy overhead. Furthermore, we show practical cases where designs mapped for optimal parallelism use 4.7 × less energy than designs using a single processing element.

FPGAs have the advantage that a single component can be configured post-fabrication to implement almost any computation. However, designing a one-size-fits-all memory architecture causes an inherent mismatch between the needs of the application and the memory sizes and placement on the architecture. Nonetheless, we show that an energy-balanced design for FPGA memory architecture (memory block size(s), memory banking, and spacing between memory banks) can guarantee that the energy is always within a factor of 2 of the optimally-matched architecture. On a combination of the VTR 7 benchmarks and a set of tunable benchmarks, we show that an architecture with internally-banked 8Kb and 256Kb memory blocks has a 31% worst-case energy overhead (8% geomean). In contrast, monolithic 16Kb memories (comparable to 18Kb and 20Kb memories used in commercial FPGAs) have a 147% worst-case energy overhead (24% geomean). Furthermore, on benchmarks where we can tune the parallelism in the implementation to improve energy (FFT, Matrix-Multiply, GMM, Sort, Window Filter), we show that we can reduce the energy overhead by another 13% (25% for the geomean).