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Research Article

SPCTRE: sparsity-constrained fully-digital reservoir computing architecture on FPGA

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 197-213 | Received 11 Jan 2024, Accepted 23 Jan 2024, Published online: 01 Feb 2024

References

  • Take it to the edge. Nat Electron. 2019;2(1):1–1. doi: 10.1038/s41928-019-0203-8
  • Wang X, Han Y, Leung VCM, et al. Edge AI. Singapore: Springer; 2020. doi: 10.1007/978-981-15-6186-3
  • Nakajima K, Fischer I, editors. Reservoir computing. Singapore: Springer; 2021. doi:10.1007/978-981-13-1687-6
  • Sussillo D, Abbott LF. Generating coherent patterns of activity from chaotic neural networks. Neuron. 2009, 2023/07/08;63(4):544–557. doi: 10.1016/j.neuron.2009.07.018.
  • Langton CG. Computation at the edge of chaos: phase transitions and emergent computation. Phys D Nonlinear Phenom. 1990;42(1–3):12–37. doi: 10.1016/0167-2789(90)90064-V
  • Rodan A, Tino P. Minimum complexity echo state network. IEEE Trans Neural Netw. 2011;22(1):131–144. doi: 10.1109/TNN.2010.2089641
  • Vinckier Q, Duport F, Smerieri A, et al. High-performance photonic reservoir computer based on a coherently driven passive cavity. Optica. 2015;2(5):438–446. doi: 10.1364/OPTICA.2.000438
  • Akai-Kasaya M, Takeshima Y, Kan S, et al. Performance of reservoir computing in a random network of single-walled carbon nanotubes complexed with polyoxometalate. Neuromorphic Comput Eng. 2022;2(1):014003. doi: 10.1088/2634-4386/ac4339
  • Kan S, Nakajima K, Asai T, et al. Physical implementation of reservoir computing through electrochemical reaction. Adv Sci. 2022;9(6):2104076. doi: 10.1002/advs.v9.6
  • Kubota H, Hasegawa T, Akai-Kasaya M, et al. Noise sensitivity of physical reservoir computing in a ring array of atomic switches. Nonlinear Theory Appl IEICE. 2022;13(2):373–378. doi: 10.1587/nolta.13.373
  • Soriano MC, Ortín S, Keuninckx L, et al. Delay-based reservoir computing: noise effects in a combined analog and digital implementation. IEEE Trans Neural Netw Learn Syst. 2015;26(2):388–393. doi: 10.1109/TNNLS.2014.2311855
  • Alomar ML, Canals V, Perez-Mora N, et al. Fpga-based stochastic echo state networks for time-series forecasting. Intell Neuroscience. 2016 Jan;2016. doi: 10.1155/2016/3917892.
  • Lin C, Liang Y, Yi Y. Fpga-based reservoir computing with optimized reservoir node architecture. In: 2022 23rd International Symposium on Quality Electronic Design (ISQED); 2022. p. 1–6.
  • Abe Y, Nishida K, Ando K, et al. Sparsity-centric reservoir computing architecture. In: KJCCS 2024; Jan. NOLTA IEICE; 2024. p. Paper ID: 62.
  • Gao C, Neil D, Ceolini E, et al. Deltarnn: A power-efficient recurrent neural network accelerator. In: Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays; New York, NY, USA. Association for Computing Machinery; 2018. p. 21–30; FPGA '18. doi: 10.1145/3174243.3174261.
  • Chen YH, Emer J, Sze V. Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks. SIGARCH Comput Archit News. 2016;44(3):367–379. doi: 10.1145/3007787.3001177
  • Bhattacharyya SS, Deprettere EF, Leupers R, et al. Handbook of signal processing systems. Switzerland: Springer International Publishing; 2019.  doi:10.1007/978-3-319-91734-4.
  • Abe Y, Nishida K, Akai-Kasaya M, et al. Reservoir computing with high-order polynomial activation functions and regenerative internal weights for enhancing nonlinear capacity and hardware resource efficiency. In: NOLTA'23; Sep. 26-29; Cittadella Campus of the University, Catania, Italy. NOLTA IEICE; 2023. p. 447–450.
  • Gupta S, Chakraborty S, Thakur CS. Neuromorphic time-multiplexed reservoir computing with on-the-fly weight generation for edge devices. IEEE Trans Neural Netw Learn Syst. 2022;33(6):2676–2685. doi: 10.1109/TNNLS.2021.3085165
  • Jaeger H. Short term memory in echo state networks. GMD Report; 2002 01.
  • Atiya A, Parlos A. New results on recurrent network training: unifying the algorithms and accelerating convergence. IEEE Trans Neural Netw. 2000;11(3):697–709. doi: 10.1109/72.846741
  • Galán-Prado F, Font-Rosselló J, Rosselló JL. Tropical reservoir computing hardware. IEEE Trans Circuits Syst II Express Briefs. 2020;67(11):2712–2716.
  • Alomar ML, Soriano MC, Escalona-Morán M, et al. Digital implementation of a single dynamical node reservoir computer. IEEE Trans Circuits Syst II Express Briefs. 2015;62(10):977–981.