Neuromorphic computing: Difference between revisions

Revision as of 22:41, 3 April 2024

Revision as of 22:16, 9 April 2024

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===Neuromemristive systems===

Neuromemristive systems isare a subclass of neuromorphic computing systems that focuses on the use of memristors to implement [[neuroplasticity]]. While neuromorphic engineering focuses on mimicking biological behavior, neuromemristive systems focus on abstraction.<ref>{{Cite web|url=https://digitalops.sandia.gov/Mediasite/Play/a10cf6ceb55d47608bb8326dd00e46611d|title=002.08 N.I.C.E. Workshop 2014: Towards Intelligent Computing with Neuromemristive Circuits and Systems – Feb. 2014|website=digitalops.sandia.gov|access-date=2019-08-26}}</ref> For example, a neuromemristive system may replace the details of a [[Cerebral cortex|cortical]] microcircuit's behavior with an abstract neural network model.<ref>C. Merkel and D. Kudithipudi, "Neuromemristive extreme learning machines for pattern classification," ISVLSI, 2014.</ref>

There exist several neuron inspired threshold logic functions<ref name="Maan 1–13"/> implemented with memristors that have applications in high level [[pattern recognition]] applications. Some of the applications reported recently include [[speech recognition]],<ref>{{Cite journal|title = Memristor pattern recogniser: isolated speech word recognition|journal = Electronics Letters|pages = 1370–1372|volume = 51|issue = 17|doi = 10.1049/el.2015.1428|first1 = A.K.|last1 = Maan|first2 = A.P.|last2 = James|first3 = S.|last3 = Dimitrijev|year = 2015|bibcode = 2015ElL....51.1370M|hdl = 10072/140989|s2cid = 61454815|hdl-access = free}}</ref> [[face recognition]]<ref>{{Cite journal|title = Memristive Threshold Logic Face Recognition|journal = Procedia Computer Science|date = 2014-01-01|pages = 98–103|volume = 41|series = 5th Annual International Conference on Biologically Inspired Cognitive Architectures, 2014 BICA|doi = 10.1016/j.procs.2014.11.090|first1 = Akshay Kumar|last1 = Maan|first2 = Dinesh S.|last2 = Kumar|first3 = Alex Pappachen|last3 = James|doi-access = free|hdl = 10072/68372|hdl-access = free}}</ref> and [[object recognition]].<ref>{{Cite journal|title = Memristive Threshold Logic Circuit Design of Fast Moving Object Detection|journal = IEEE Transactions on Very Large Scale Integration (VLSI) Systems|date = 2015-10-01|issn = 1063-8210|pages = 2337–2341|volume = 23|issue = 10|doi = 10.1109/TVLSI.2014.2359801|first1 = A.K.|last1 = Maan|first2 = D.S.|last2 = Kumar|first3 = S.|last3 = Sugathan|first4 = A.P.|last4 = James|arxiv = 1410.1267|s2cid = 9647290}}</ref> They also find applications in replacing conventional digital logic gates.<ref>{{Cite journal|title = Resistive Threshold Logic|journal = IEEE Transactions on Very Large Scale Integration (VLSI) Systems|date = 2014-01-01|issn = 1063-8210|pages = 190–195|volume = 22|issue = 1|doi = 10.1109/TVLSI.2012.2232946|first1 = A.P.|last1 = James|first2 = L.R.V.J.|last2 = Francis|first3 = D.S.|last3 = Kumar|arxiv = 1308.0090|s2cid = 7357110}}</ref><ref>{{Cite journal|title = Threshold Logic Computing: Memristive-CMOS Circuits for Fast Fourier Transform and Vedic Multiplication|journal = IEEE Transactions on Very Large Scale Integration (VLSI) Systems|date = 2015-11-01|issn = 1063-8210|pages = 2690–2694|volume = 23|issue = 11|doi = 10.1109/TVLSI.2014.2371857|first1 = A.P.|last1 = James|first2 = D.S.|last2 = Kumar|first3 = A.|last3 = Ajayan|arxiv = 1411.5255|s2cid = 6076956}}</ref>

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===Neuromorphic sensors===

The concept of neuromorphic systems can be extended to sensors (not just to computation). An example of this applied to detecting [[light]] is the [[retinomorphic sensor]] or, when employed in an array, the [[event camera]]. An event camera's pixels all register changes in brightness levels individually, which makes these cameras comparable to human eyesight in their theoretical power consumption<ref>{{Cite journal |last=Skorka |first=Orit |date=2011-07-01 |title=Toward a digital camera to rival the human eye |url=http://electronicimaging.spiedigitallibrary.org/article.aspx?doi=10.1117/1.3611015 |journal=Journal of Electronic Imaging |language=en |volume=20 |issue=3 |pages=033009 |doi=10.1117/1.3611015 |issn=1017-9909}}</ref>. In 2022, researchers from the [[Max Planck Institute for Polymer Research]] reported an organic artificial spiking neuron that exhibits the signal diversity of biological neurons while operating in the biological wetware, thus enabling ''in-situ'' neuromorphic sensing and biointerfacing applications.<ref>{{cite journal |last1=Sarkar |first1=Tanmoy |last2=Lieberth |first2=Katharina |last3=Pavlou |first3=Aristea |last4=Frank |first4=Thomas |last5=Mailaender |first5=Volker |last6=McCulloch |first6=Iain |last7=Blom |first7=Paul W. M. |last8=Torriccelli |first8=Fabrizio |last9=Gkoupidenis |first9=Paschalis |title=An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing |journal=Nature Electronics |date=7 November 2022 |volume=5 |issue=11 |pages=774–783 |doi=10.1038/s41928-022-00859-y |s2cid=253413801 |language=en |issn=2520-1131|doi-access=free |hdl=10754/686016 |hdl-access=free }}</ref><ref>{{cite journal |title=Artificial neurons emulate biological counterparts to enable synergetic operation |journal=Nature Electronics |date=10 November 2022 |volume=5 |issue=11 |pages=721–722 |doi=10.1038/s41928-022-00862-3 |s2cid=253469402 |url=https://www.nature.com/articles/s41928-022-00862-3 |language=en |issn=2520-1131}}</ref>

=== Military applications ===