{"id":152119,"date":"2022-12-07T21:26:00","date_gmt":"2022-12-08T03:26:00","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2022\/12\/computing-with-chemicals-makes-faster-leaner-ai"},"modified":"2022-12-07T21:26:00","modified_gmt":"2022-12-08T03:26:00","slug":"computing-with-chemicals-makes-faster-leaner-ai","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2022\/12\/computing-with-chemicals-makes-faster-leaner-ai","title":{"rendered":"Computing with Chemicals Makes Faster, Leaner AI"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/computing-with-chemicals-makes-faster-leaner-ai.jpg\"><\/a><\/p>\n<p>How far away could an artificial brain be? Perhaps a very long way off still, but a working analogue to the essential element of the brain\u2019s networks, the synapse, appears closer at hand now.<\/p>\n<p>That\u2019s because a device that draws inspiration from batteries now appears surprisingly well suited to run artificial neural networks. Called electrochemical RAM (ECRAM), it is giving traditional transistor-based AI an unexpected run for its money\u2014and is quickly moving toward the head of the pack in the race to develop the perfect artificial synapse. Researchers recently reported a string of advances at this week\u2019s IEEE International Electron Device Meeting (<a href=\"https:\/\/www.ieee-iedm.org\/\" target=\"_blank\" class=\"\">IEDM 2022<\/a>) and elsewhere, including ECRAM devices that use less energy, hold memory longer, and take up less space.<\/p>\n<p>The artificial neural networks that power today\u2019s machine-learning algorithms are software that models a large collection of electronics-based \u201cneurons,\u201d along with their many connections, or synapses. Instead of representing neural networks in software, researchers think that faster, more energy-efficient AI would result from representing the components, especially the synapses, with real devices. This concept, called analog AI, requires a memory cell that combines a whole slew of difficult-to-obtain properties: it needs to hold a large enough range of analog values, switch between different values reliably and quickly, hold its value for a long time, and be amenable to manufacturing at scale.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>How far away could an artificial brain be? Perhaps a very long way off still, but a working analogue to the essential element of the brain\u2019s networks, the synapse, appears closer at hand now. That\u2019s because a device that draws inspiration from batteries now appears surprisingly well suited to run artificial neural networks. Called electrochemical [\u2026]<\/p>\n","protected":false},"author":556,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19,6],"tags":[],"class_list":["post-152119","post","type-post","status-publish","format-standard","hentry","category-chemistry","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/152119","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/556"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=152119"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/152119\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=152119"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=152119"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=152119"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}