Editing Analog computer (section)

==Resurgence==
[[File:THE ANALOG THING.jpg|thumb|alt=Modern analog computer: THE ANALOG THING|Modern analog computer: [[THE ANALOG THING]]]]
With the development of [[very-large-scale integration]] (VLSI) technology, Yannis Tsividis' group at Columbia University has been revisiting analog/hybrid computers design in standard CMOS process. Two VLSI chips have been developed, an 80th-order analog computer (250&nbsp;nm) by Glenn Cowan<ref name="lBZ6E">{{Cite web|title=Glenn Cowan|url=http://users.encs.concordia.ca/~gcowan/index.html |publisher=Concordia.ca |access-date=2016-02-05}}</ref> in 2005<ref name="eRX1N">{{Cite book|date=2005-02-01|pages=82–586 |volume=1 |doi=10.1109/ISSCC.2005.1493879 |first1=G.E.R.|last1=Cowan |first2=R.C.|last2=Melville|first3=Y.|last3=Tsividis|title=ISSCC. 2005 IEEE International Digest of Technical Papers. Solid-State Circuits Conference, 2005 |chapter=A VLSI analog computer/Math co-processor for a digital computer |isbn = 978-0-7803-8904-5|s2cid=38664036}}</ref> and a 4th-order hybrid computer (65&nbsp;nm) developed by Ning Guo in 2015,<ref name="WrRKX">{{Cite book |date=2015-09-01 |pages=279–282|doi=10.1109/ESSCIRC.2015.7313881|first1=Ning|last1=Guo|first2=Yipeng|last2=Huang|first3=Tao|last3=Mai|first4=S.|last4=Patil |first5=Chi|last5=Cao|first6=Mingoo|last6=Seok|first7=S. |last7=Sethumadhavan |first8=Y.|last8=Tsividis|title=ESSCIRC Conference 2015 - 41st European Solid-State Circuits Conference (ESSCIRC) |chapter=Continuous-time hybrid computation with programmable nonlinearities |isbn = 978-1-4673-7470-5|s2cid=16523767}}</ref> both targeting at energy-efficient ODE/PDE applications. Glenn's chip contains 16 macros, in which there are 25 analog computing blocks, namely integrators, multipliers, fanouts, few nonlinear blocks. Ning's chip contains one macro block, in which there are 26 computing blocks including integrators, multipliers, fanouts, ADCs, SRAMs and DACs. Arbitrary nonlinear function generation is made possible by the ADC+SRAM+DAC chain, where the SRAM block stores the nonlinear function data. The experiments from the related publications revealed that VLSI analog/hybrid computers demonstrated about 1–2 orders magnitude of advantage in both solution time and energy while achieving accuracy within 5%, which points to the promise of using analog/hybrid computing techniques in the area of energy-efficient approximate computing.{{citation needed|date=November 2017}} In 2016, a team of researchers developed a compiler to solve [[differential equation]]s using analog circuits.<ref name="AZUJH">{{cite web|url=https://news.mit.edu/2016/analog-computing-organs-organisms-0620|title=Analog computing returns|date=20 June 2016 }}</ref>

Analog computers are also used in [[neuromorphic computing]], and in 2021 a group of researchers have shown that a specific type of [[artificial neural network]] called a [[spiking neural network]] was able to work with analog neuromorphic computers.<ref>{{cite journal|title=Surrogate gradients for analog neuromorphic computing|author1=Benjamin Cramer|author2=Sebastian Billaudelle|author3=Simeon Kanya|author4=Aron Leibfried|author5=Andreas Grübl|author6=Vitali Karasenko|author7=Christian Pehle|author8=Korbinian Schreiber|author9=Yannik Stradmann|author10=Johannes Weis|author11=Johannes Schemmel|author12=View ORCID ProfileFriedemann Zenke|journal=PNAS|volume=119|issue=4|doi=10.1073/pnas.2109194119|date=January 25, 2022|doi-access=free |pmid=35042792 |pmc=8794842 |bibcode=2022PNAS..11909194C }}</ref>

In 2021, the German company [[anabrid]] GmbH began to produce [[THE ANALOG THING]] (abbreviated THAT), a small low-cost analog computer mainly for educational and scientific use.<ref>{{cite web |title=The Analog Thing: Newsletter #1 |url=https://the-analog-thing.org/newsletter/1/ |website=the-analog-thing.org}}</ref> The company is also constructing analog [[Mainframe computer|mainframes]] and [[hybrid computer]]s.