Name
Abstract | ||
|---|---|---|
Recently FPGA researchers have proposed different approaches to enable dynamic voltage scaling (DVS) for FPGAs. While the proposed approaches have shown that DVS is able to significantly reduce FPGA power consumption, most of these solutions were developed only for the soft fabric of the FPGA and hence cannot be deployed for applications that use the FPGA hard blocks such as block RAMs (BRAMs). In this work, we extend a previously proposed offline calibration-based DVS approach to enable DVS for FPGAs with BRAMs; we build testing circuitry to ensure that all used BRAM cells operate safely while scaling the supply voltage, and we develop testing procedures that are able to measure the delay of timing paths that start or end at BRAMs. We extend the CAD tool FRoC to automatically generate calibration designs with BRAM testers along with soft fabric testers to measure the actual Fmax of each application on any chip under different operating conditions; this information is stored in a calibration table that is then used when the application is running to scale the supply voltage to the minimum value that guarantees safe operation at the desired speed. Using our proposed solution, we show that we can run a discrete Fourier transform core with 32 % and 46 % power reduction compared to the conventional fixed-voltage operation at the reported F_max and at a lower clock frequency, respectively. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/FPL.2018.00020 | 2018 28th International Conference on Field Programmable Logic and Applications (FPL) |
Keywords | Field | DocType |
FPGA,DVS,BRAM testing,low power | Dynamic voltage scaling,Computer science,Voltage,Field-programmable gate array,Chip,Real-time computing,Discrete Fourier transform,Computer hardware,Scaling,Clock rate,Calibration | Conference |
ISSN | ISBN | Citations |
1946-147X | 978-1-5386-8518-1 | 1 |
PageRank | References | Authors |
0.35 | 21 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ibrahim Ahmed | 1 | 12 | 3.31 |
| Shuze Zhao | 2 | 6 | 1.86 |
| James Meijers | 3 | 1 | 1.36 |
| Olivier Trescases | 4 | 1 | 1.02 |
| Vaughn Betz | 5 | 22 | 7.86 |