Neuromorphic Computing, Architectures, Models, and Applications. A Beyond-CMOS Approach to Future Computing, June 29-July 1, 2016, Oak Ridge, TN

Potok, Thomas E.; Schuman, Catherine D.; Patton, Robert M.; Hylton, Todd; Li, Hai; Pino, Robinson E.

doi:10.2172/1341738

Cited by 7 publications

(6 citation statements)

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“…For example, although neurons and synapses have been chosen as the primary computational units of neuromorphic computers, there are a variety of other types of neural components that may be useful for computation, including glial cells 16,17 . Moreover, neurons and synapses have been a convenient level of abstraction for neuromorphic computers, but whether they are the most appropriate level of abstraction is still an open question 18 .…”

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confidence: 99%

Opportunities for neuromorphic computing algorithms and applications

et al. 2022

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Neuromorphic computing technologies will be important for the future of computing, but much of the work in neuromorphic computing has focused on hardware development. Here, we review recent results in neuromorphic computing algorithms and applications. We highlight characteristics of neuromorphic computing technologies that make them attractive for the future of computing and we discuss opportunities for future development of algorithms and applications on these systems.

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confidence: 99%

Opportunities for neuromorphic computing algorithms and applications

et al. 2022

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“…[1] Thus, artificial platforms analogous to brain-like information processing and memory operations [2] are now becoming a reality with devices based on nonlinear dynamics. [3][4][5] High-dimensional nonlinear information obtained as a function of current or voltage output can be treated equivalent to the information generated from software-designed NN architectures when operated by nonlinear activation functions. As such, the only algorithm implementation can be reduced to the training of these current/voltage outputs without the need of a separately designed information processing unit.…”

Section: Introductionmentioning

confidence: 99%

Emergence of In‐Materio Intelligence from an Incidental Structure of a Single‐Walled Carbon Nanotube–Porphyrin Polyoxometalate Random Network

Banerjee

Kotooka

Azhari

et al. 2022

Advanced Intelligent Systems

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Hardware‐based machine intelligence with the network architecture of reservoir computing (RC) is gaining interest because of its biological computational resemblance along with an easy and efficient neural network training approach. Herein, such a physical RC (in‐materio RC) platform consisting of a recurrent network formed by the single‐walled carbon nanotube (SWNT)–porphyrin polyoxometalate (Por–POM) complex is demonstrated. The network architecture executes the fundamental reservoir properties of nonlinearity, higher harmonic generation, and 1/fγ power law information processing ability. Based on these functionalities, an RC benchmark task of waveform generation is performed where the device achieves maximum fitting accuracy of 99.4%. Furthermore, a supervised object classification task based on a one‐hot vector target is also executed using Toyota Human Support Robot tactile inputs. The successful classification of objects of different hardness is enhanced when the device output response follows the 1/fγ power law of maximized information processing.

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“…In summary, while more data than ever before is produced, we are simultaneously faced with the end of Moore's law, limited performance due to the Von Neumann bo leneck, and an increasing energy consumption (with corresponding carbon footprint) [15].…”

Section: Introductionmentioning

confidence: 99%

On the computational power and complexity of Spiking Neural Networks

Kwisthout¹,

Donselaar²

2020

Preprint

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e last decade has seen the rise of neuromorphic architectures based on arti cial spiking neural networks, such as the SpiNNaker, TrueNorth, and Loihi systems.e massive parallelism and colocating of computation and memory in these architectures potentially allows for an energy usage that is orders of magnitude lower compared to traditional Von Neumann architectures. However, to date a comparison with more traditional computational architectures (particularly with respect to energy usage) is hampered by the lack of a formal machine model and a computational complexity theory for neuromorphic computation. In this paper we take the rst steps towards such a theory. We introduce spiking neural networks as a machine model where-in contrast to the familiar Turing machine-information and the manipulation thereof are co-located in the machine. We introduce canonical problems, de ne hierarchies of complexity classes and provide some rst completeness results.

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Neuromorphic Computing, Architectures, Models, and Applications. A Beyond-CMOS Approach to Future Computing, June 29-July 1, 2016, Oak Ridge, TN

Cited by 7 publications

References 0 publications

Opportunities for neuromorphic computing algorithms and applications

Opportunities for neuromorphic computing algorithms and applications

Emergence of In‐Materio Intelligence from an Incidental Structure of a Single‐Walled Carbon Nanotube–Porphyrin Polyoxometalate Random Network

On the computational power and complexity of Spiking Neural Networks

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