Neuromorphic computing: Difference between revisions

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{{Use mdy dates|date = January 2019}}

'''Neuromorphic computing''' is an approach to computing that is inspired by the structure and function of the human brain.<ref>{{Cite journal |last1=Ham |first1=Donhee |last2=Park |first2=Hongkun |last3=Hwang |first3=Sungwoo |last4=Kim |first4=Kinam |title=Neuromorphic electronics based on copying and pasting the brain |url=https://www.nature.com/articles/s41928-021-00646-1 |journal=Nature Electronics |year=2021 |language=en |volume=4 |issue=9 |pages=635–644 |doi=10.1038/s41928-021-00646-1 |s2cid=240580331 |issn=2520-1131}}</ref><ref>{{Cite journal |last1=van de Burgt |first1=Yoeri |last2=Lubberman |first2=Ewout |last3=Fuller |first3=Elliot J. |last4=Keene |first4=Scott T. |last5=Faria |first5=Grégorio C. |last6=Agarwal |first6=Sapan |last7=Marinella |first7=Matthew J. |last8=Alec Talin |first8=A. |last9=Salleo |first9=Alberto |date=April 2017 |title=A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing |url=https://www.nature.com/articles/nmat4856 |journal=Nature Materials |language=en |volume=16 |issue=4 |pages=414–418 |doi=10.1038/nmat4856 |pmid=28218920 |bibcode=2017NatMa..16..414V |issn=1476-4660}}</ref> A neuromorphic computer/chip is any device that uses physical [[artificial neuron]]s to do computations.<ref>{{cite journal|last1=Mead|first1=Carver|title=Neuromorphic electronic systems|journal=Proceedings of the IEEE|date=1990|volume=78|issue=10|pages=1629–1636|doi=10.1109/5.58356|s2cid=1169506 |url=https://authors.library.caltech.edu/53090/1/00058356.pdf}}</ref><ref name=":2" /> In recent times, the term ''neuromorphic'' has been used to describe [[Analogue electronics|analog]], [[Digital electronics|digital]], [[Mixed-signal integrated circuit|mixed-mode analog/digital VLSI]], and software systems that implement models of [[neural system]]s (for [[perception]], [[motor control]], or [[multisensory integration]]). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based [[memristor]]s,<ref name="Maan 1–13">{{Cite journal|last1=Maan|first1=A. K.|last2=Jayadevi|first2=D. A.|last3=James|first3=A. P.|date=2016-01-01|title=A Survey of Memristive Threshold Logic Circuits|journal=IEEE Transactions on Neural Networks and Learning Systems|volume=PP|issue=99|pages=1734–1746|doi=10.1109/TNNLS.2016.2547842|pmid=27164608|issn=2162-237X|arxiv=1604.07121|bibcode=2016arXiv160407121M|s2cid=1798273}}</ref> [[Spintronics|spintronic]] memories, threshold switches, [[transistor]]s,<ref>{{Cite journal|title = Mott Memory and Neuromorphic Devices|journal = Proceedings of the IEEE|date = 2015-08-01|issn = 0018-9219|pages = 1289–1310|volume = 103|issue = 8|doi = 10.1109/JPROC.2015.2431914|first1 = You|last1 = Zhou|first2 = S.|last2 = Ramanathan|s2cid = 11347598|url=https://zenodo.org/record/895565}}</ref><ref name=":2">{{Cite conference|author=Rami A. Alzahrani|author2=Alice C. Parker|title=Neuromorphic Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling |conference=International Conference on Neuromorphic Systems 2020|date=July 2020|pppages=1-81–8|language=EN|doi=10.1145/3407197.3407204|s2cid=220794387|doi-access=free}}</ref> among others. Training software-based neuromorphic systems of [[spiking neural networks]] can be achieved using error backpropagation, e.g., using [[Python (programming language)|Python]] based frameworks such as snnTorch,<ref>{{cite arxivarXiv|last1=Eshraghian|first1=Jason K.|last2=Ward|first2=Max|last3=Neftci |first3=Emre|last4=Wang|first4=Xinxin|last5=Lenz|first5=Gregor|last6=Dwivedi|first6=Girish|last7=Bennamoun|first7=Mohammed|last8=Jeong|first8=Doo Seok|last9=Lu|first9=Wei D.|title=Training Spiking Neural Networks Using Lessons from Deep Learning |date=1 October 2021 |arxivclass=cs.NE |eprint=2109.12894 }}</ref> or using canonical learning rules from the biological learning literature, e.g., using BindsNet.<ref>{{Cite web |url=https://github.com/Hananel-Hazan/bindsnet | title=Hananel-Hazan/bindsnet: Simulation of spiking neural networks (SNNs) using PyTorch.| website=[[GitHub]]| date=31 March 2020}}</ref>

A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how [[information]] is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.

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The European Union funded a series of projects at the University of Heidelberg, which led to the development of [[BrainScaleS]] (brain-inspired multiscale computation in neuromorphic hybrid systems), a hybrid analog [[neuromorphic]] supercomputer located at Heidelberg University, Germany. It was developed as part of the Human Brain Project neuromorphic computing platform and is the complement to the [[SpiNNaker]] supercomputer (which is based on digital technology). The architecture used in BrainScaleS mimics biological neurons and their connections on a physical level; additionally, since the components are made of silicon, these model neurons operate on average 864 times (24 hours of real time is 100 seconds in the machine simulation) that of their biological counterparts.<ref>{{Cite web|date=2016-03-21|title=Beyond von Neumann, Neuromorphic Computing Steadily Advances|url=https://www.hpcwire.com/2016/03/21/lacking-breakthrough-neuromorphic-computing-steadily-advance/|access-date=2021-10-08|website=HPCwire|language=en-US}}</ref>

In 2019, the European Union funded the project "Neuromorphic quantum computing"<ref>{{Cite journal |title=Neuromrophic Quantum Computing {{!}} Quromorphic Project {{!}} Fact Sheet {{!}} H2020 |url=https://cordis.europa.eu/project/id/828826 |access-date=2024-03-18 |website=CORDIS {{!}} European Commission |language=en |doi=10.3030/828826}}</ref> exploring the use of neuromorphic computing to perform quantum operations. Neuromorphic quantum computing<ref>{{Citation |last1=Pehle |first1=Christian |title=Neuromorphic quantum computing |date=2021-03-30 |url=http://arxiv.org/abs/2005.01533 |access-date=2024-03-18 |arxiv=2005.01533 |last2=Wetterich |first2=Christof}}</ref> (abbreviated as 'n.quantum computing') is an [[unconventional computing]] type of computing that uses neuromorphic computing to perform quantum operations.<ref>{{Cite journal |last=Wetterich |first=C. |date=2019-11-01 |title=Quantum computing with classical bits |url=https://www.sciencedirect.com/science/article/pii/S0550321319302627 |journal=Nuclear Physics B |volume=948 |pages=114776 |doi=10.1016/j.nuclphysb.2019.114776 |issn=0550-3213|arxiv=1806.05960 }}</ref><ref>{{Citation |last1=Pehle |first1=Christian |title=Emulating quantum computation with artificial neural networks |date=2018-10-24 |url=http://arxiv.org/abs/1810.10335 |access-date=2024-03-18 |arxiv=1810.10335 |last2=Meier |first2=Karlheinz |last3=Oberthaler |first3=Markus |last4=Wetterich |first4=Christof}}</ref> It was suggested that [[quantum algorithm]]s, which are algorithms that run on a realistic model of [[Quantum computing|quantum computation]], can be computed equally efficiently with neuromorphic quantum computing.<ref>{{Cite journal |last1=Carleo |first1=Giuseppe |last2=Troyer |first2=Matthias |date=2017-02-10 |title=Solving the quantum many-body problem with artificial neural networks |url=https://www.science.org/doi/10.1126/science.aag2302 |journal=Science |language=en |volume=355 |issue=6325 |pages=602–606 |doi=10.1126/science.aag2302 |pmid=28183973 |issn=0036-8075|arxiv=1606.02318 }}</ref><ref>{{Cite journal |last1=Torlai |first1=Giacomo |last2=Mazzola |first2=Guglielmo |last3=Carrasquilla |first3=Juan |last4=Troyer |first4=Matthias |last5=Melko |first5=Roger |last6=Carleo |first6=Giuseppe |date=May 2018 |title=Neural-network quantum state tomography |url=https://www.nature.com/articles/s41567-018-0048-5 |journal=Nature Physics |language=en |volume=14 |issue=5 |pages=447–450 |doi=10.1038/s41567-018-0048-5 |issn=1745-2481|arxiv=1703.05334 }}</ref><ref>{{Cite journal |last1=Sharir |first1=Or |last2=Levine |first2=Yoav |last3=Wies |first3=Noam |last4=Carleo |first4=Giuseppe |last5=Shashua |first5=Amnon |date=2020-01-16 |title=Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems |url=https://link.aps.org/doi/10.1103/PhysRevLett.124.020503 |journal=Physical Review Letters |volume=124 |issue=2 |pages=020503 |doi=10.1103/PhysRevLett.124.020503|pmid=32004039 |arxiv=1902.04057 }}</ref><ref>{{Citation |last1=Broughton |first1=Michael |title=TensorFlow Quantum: A Software Framework for Quantum Machine Learning |date=2021-08-26 |url=http://arxiv.org/abs/2003.02989 |access-date=2024-03-18 |arxiv=2003.02989 |last2=Verdon |first2=Guillaume |last3=McCourt |first3=Trevor |last4=Martinez |first4=Antonio J. |last5=Yoo |first5=Jae Hyeon |last6=Isakov |first6=Sergei V. |last7=Massey |first7=Philip |last8=Halavati |first8=Ramin |last9=Niu |first9=Murphy Yuezhen}}</ref><ref name="Di Ventra">{{Citation |last=Di Ventra |first=Massimiliano |title=MemComputing vs. Quantum Computing: some analogies and major differences |date=2022-03-23 |url=http://arxiv.org/abs/2203.12031 |access-date=2024-03-18 |arxiv=2203.12031}}</ref> Both, traditional quantum computing and neuromorphic quantum computing are physics-based unconventional computing approaches to computations and do not follow the [[von Neumann architecture]]. They both construct a system (a circuit) that represents the physical problem at hand, and then leverage their respective physics properties of the system to seek the "minimum". Neuromorphic quantum computing and quantum computing share similar physical properties during computation.<ref name="Di Ventra"/><ref>{{Cite journal |last1=Wilkinson |first1=Samuel A. |last2=Hartmann |first2=Michael J. |date=2020-06-08 |title=Superconducting quantum many-body circuits for quantum simulation and computing |url=https://doi.org/10.1063/5.0008202 |journal=Applied Physics Letters |volume=116 |issue=23 |doi=10.1063/5.0008202 |issn=0003-6951|arxiv=2003.08838 }}</ref>

[[Brainchip]] announced in October 2021 that it was taking orders for its Akida AI Processor Development Kits<ref>{{Cite web|url=https://cdn-api.markitdigital.com/apiman-gateway/ASX/asx-research/1.0/file/2924-02438858-2A1332482?access_token=83ff96335c2d45a094df02a206a39ff4|title=Taking Orders of Akida AI Processor Development Kits|date=21 October 2021}}</ref> and in January 2022 that it was taking orders for its Akida AI Processor PCIe boards,<ref>{{cite web | url=https://www.electronics-lab.com/first-mini-pciexpress-board-with-spiking-neural-network-chip/ | title=First mini PCIexpress board with spiking neural network chip | date=January 19, 2022 }}</ref> making it the world's first commercially available neuromorphic processor.