Editing Neural network (machine learning) (section)

===Hardware===
Large and effective neural networks require considerable computing resources.<ref name=":0">{{cite journal|last1=Edwards|first1=Chris|s2cid=11026540|title=Growing pains for deep learning|journal=Communications of the ACM|date=25 June 2015|volume=58|issue=7|pages=14–16|doi=10.1145/2771283}}</ref> While the brain has hardware tailored to the task of processing signals through a [[Graph (discrete mathematics)|graph]] of neurons, simulating even a simplified neuron on [[von Neumann architecture]] may consume vast amounts of [[Random-access memory|memory]] and storage. Furthermore, the designer often needs to transmit signals through many of these connections and their associated neurons{{snd}} which require enormous [[Central processing unit|CPU]] power and time.{{citation needed|date=October 2024}}

Some argue that the resurgence of neural networks in the twenty-first century is largely attributable to advances in hardware: from 1991 to 2015, computing power, especially as delivered by [[General-purpose computing on graphics processing units|GPGPUs]] (on [[Graphics processing unit|GPUs]]), has increased around a million-fold, making the standard backpropagation algorithm feasible for training networks that are several layers deeper than before.<ref name="SCHIDHUB4"/> The use of accelerators such as [[Field-programmable gate array|FPGA]]s and GPUs can reduce training times from months to days.{{r|:0}}<ref>{{Cite web |title=The Bitter Lesson |url=http://www.incompleteideas.net/IncIdeas/BitterLesson.html |access-date=7 August 2024 |website=incompleteideas.net}}</ref>

[[Neuromorphic engineering]] or a [[physical neural network]] addresses the hardware difficulty directly, by constructing non-von-Neumann chips to directly implement neural networks in circuitry. Another type of chip optimized for neural network processing is called a [[Tensor Processing Unit]], or TPU.<ref>{{cite news |url=https://www.wired.com/2016/05/google-tpu-custom-chips/ |author=Cade Metz |newspaper=Wired |date=18 May 2016 |title=Google Built Its Very Own Chips to Power Its AI Bots |access-date=5 March 2017 |archive-date=13 January 2018 |archive-url=https://web.archive.org/web/20180113150305/https://www.wired.com/2016/05/google-tpu-custom-chips/ |url-status=live }}</ref>