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spiking-neuron integrated circuit with a scalable communication network and interface. Science. 345, 668–673 (2014)
31. Indiveri, G., Linares-Barranco, B., Hamilton, T., van Schaik, A., Etienne-Cummings, R., et al.:
Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 1–23 (2011)
32. Shimokawa, Y., Fuwa, Y., Aramaki N. A parallel ASIC VLSI neurocomputer for a large
number of neurons and billion connections per second speed. In: IEEE International Joint
Conference on Neural Networks. Singapore (1991)
33. Omondi, A.R., Rajapakse, J.C.: FPGA Implementations of Neural Networks. Springer, Dordrecht (2006)
34. Nurvitadhi, E., Sheffield, D., Sim, J., Mishra, A., Venkatesh, G., Marr, D. Accelerating
binarized neural networks: comparison of FPGA, CPU, GPU, and ASIC. In: International
Conference on Field-Programmable Technology (FPT), IEEE (2016)
35. Qiao, Y., Shen, J., Xiao, T., Yang, Q., Wen, M., Zhang, C.: FPGA-accelerated deep
convolutional neural networks for high throughput and energy efficiency. Concurr. Comput.
Pract. Exp. 29 (2016)
36. NVIDIA launches the world’s first graphics processing unit: GeForce 256 [Online]. http://www.
nvidia.com/object/IO_20020111_5424.html (1999)
37. Jager, C.: Nvidia unveils Volta: the most powerful GPU ever [online]. https://www.lifehacker.
com.au/2017/05/nvidias-unveils-volta-gv100-the-most-powerful-gpu-ever/ (2017)
38. Krizhevsky, A., Sutskever, I., Hinton, G. E.: Imagenet classification with deep convolutional
neural networks. In: Advances in Neural Information Processing Systems (2012)
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[online]. https://blogs.nvidia.com/blog/2017/07/11/deep-learning-pioneers-boost-research-atnvidia-ai-labs-around-the-world/ (2017)
40. Romero, A., et al.: Diet networks: thin parameters for fat genomics. ArXiv (2017)
41. Finn, C., Levine, S.: Deep visual foresight for planning robot motion. ArXiv (2017)
42. Meier, K.: The FACETS project. Available https://facets.kip.uni-heidelberg.de/images/4/48/
Public%2D%2DFACETS_15879_Summary-flyer.pdf (2010)
43. Qualcomm helps make your mobile devices smarter with new Snapdragon machine learning
software development kit. https://www.qualcomm.com/news/releases/2016/05/02/qualcommhelps-make-your-mobile-devices-smarter-new-snapdragon-machine (2016)
44. Avizienis, A.V., Sillin, H.O., Martin-Olmos, C., Shieh, H.H., Aono, M., Stieg, A.Z.,
Gimzewski, J.K.: Neuromorphic atomic switch networks. PLoS One. 7(8), e42772 (2012)
45. Yang, J.J., Strukov, D.B., Stewart, D.R.: Memristive devices for computing. Nat. Nanotechnol.
8, 13–24 (2013)
46. Stieg, A.Z., et al.: Self-organization and emergence of dynamical structures in neuromorphic
atomic switch networks. In: Adamatzky, A., Chua, L. (eds.) Memristor Networks. Springer,
Cham (2014)
47. Demis, E.C., Aguilera, R., Sillin, H.O., Scharnhorst, K., Sandouk, E.J., Aono, M., Stieg, A.Z.,
Gimzewski, J.K.: Atomic switch networks nanoarchitectonic design of a complex system for
natural computing. Nanotechnology. 26, 204003 (2015)
48. Demis, E.C., Aguilera, R., Scharnhorst, K., Aono, M., Stieg, A.Z., Gimzewski, J.K.:
Nanoarchitectonic atomic switch networks for unconventional computing. Jpn. J. Appl. Phys.
55, 1102B2 (2016)
49. Sillin, H.O., Aguilera, R., Shieh, H.H., Avizienis, A.V., Aono, M., Stieg, A.Z., Gimzewski, J.
K.: A theoretical and experimental study of neuromorphic atomic switch networks for reservoir
computing. Nanotechnology. 24, 384004 (2013)
50. Scharnhorst, K.S., Carbajal, J.P., Aguilera, R.C., Sandouk, E.J., Aono, M., Stieg, A.Z.,
Gimzewski, J.K.: Atomic switch networks as complex adaptive systems. Jpn. J. Appl. Phys.
57, 03ED02 (2018)
Atomic Switch Networks for Neuroarchitectonics: Past, Present, Future
241