AMF-Placer  2.0
An Open-Source Timing-driven Analytical Mixed-size FPGA Placer
Benchmarks Details

Benchmark Details

As mentioned in the introduction, we collect the latest practical open-source benchmarks from various domains.

Benchmarks Rosetta FaceDetection SpooNN OptimSoC MiniMap2 OpenPiton MemN2N BLSTM Rosetta DigitRecog
#LUT 68945 63095 186183 407586 180388 184997 118967 151636
#FF 56987 70987 248983 252624 111966 84694 54690 105580
#CARRY 4978 2091 1715 19826 1712 11528 2762 1970
#Mux 2177 217 27037 180 13696 4466 36210 4662
#LUTRAM 255 251 901 251 752 3500 1147 251
#DSP 101 165 51 528 58 312 258 1
#BRAM 141 208 218 283 147 148 812 379
#Cell 134450 137937 468150 681889 309145 289721 215101 265775
#Macro 3582 1135 21882 8746 8278 5775 14651 3061
#siteForMacro 55666 23079 89004 191263 48066 118960 171822 55754
MacroRatio 40% 16% 19% 28% 15% 41% 80% 21%
  • Rosetta FaceDetection/DigitRecog: Y. Zhou, U. Gupta, S. Dai, R. Zhao, N. Srivastava, H. Jin, J. Featherston, Y.-H. Lai, G. Liu, G. A. Velasquez et al., “Rosetta: A realistic high-level synthesis benchmark suite for software programmable fpgas,” in Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2018, pp. 269–278.
  • SpooNN: K. Kara, “Spoonn: Fpga-based neural network inference library,” 2018.
  • OptimSoC: S. Wallentowitz, P. Wagner, M. Tempelmeier, T. Wild, and A. Herkersdorf, “Open tiled manycore system-on-chip,” arXiv preprint arXiv:1304.5081, 2013.
  • MiniMap2: L. Guo, J. Lau, Z. Ruan, P. Wei, and J. Cong, “Hardware acceleration of long read pairwise overlapping in genome sequencing: A race between fpga and gpu,” in 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 2019, pp. 127–135.
  • OpenPiton: J. Balkind, M. McKeown, Y. Fu, T. Nguyen, Y. Zhou, A. Lavrov, M. Shahrad, A. Fuchs, S. Payne, X. Liang et al., “Openpiton: An open source manycore research framework,” ACM SIGPLAN Notices, vol. 51, no. 4, pp. 217–232, 2016.
  • MemN2N: S. Sukhbaatar, A. Szlam, J. Weston, and R. Fergus, “End-to-end memory networks,” arXiv preprint arXiv:1503.08895, 2015.
  • BLSTM: V. Rybalkin, N. Wehn, M. R. Yousefi, and D. Stricker, “Hardware architecture of bidirectional long short-term memory neural network for optical character recognition,” in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017. IEEE, 2017, pp. 1390–1395.