Energy-aware Placement for SRAM-NVM Hybrid FPGAs

Abstract

Field-programmable gate arrays (FPGAs) have been widely used in many applications due to their reconfigurability. Notably, the short development time makes the FPGAs one of the promising reconfigurable architectures for emerging applications, such as deep learning. As CMOS technology advances, however, conventional SRAM-based FPGAs have reached their limitations. To overcome these obstacles, NVM-based FPGAs have been introduced. Although NVM-based FPGAs have the features of high area density, low static power consumption, and non-volatility, they are struggling to reduce energy consumption. Their challenge is mainly caused by the access speed of NVM, which is relatively slower than SRAM. In this paper, for compensating this limitation, we suggest SRAM-NVM hybrid FPGA architecture with SRAM- and NVM-based CLBs. In addition, we propose an energy-aware placement for utilizing the SRAM-NVM hybrid FPGAs. As a result of our experiments, we were able to reduce the average energy consumption of SRAM-NVM hybrid FPGA by 22.23% and 21.94% compared to SRAM-based FPGA on the MCNC and VTR benchmarks, respectively.