{"id":193272,"date":"2024-07-21T16:22:24","date_gmt":"2024-07-21T21:22:24","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2024\/07\/neural-network-training-made-easy-with-smart-hardware"},"modified":"2024-07-21T16:22:24","modified_gmt":"2024-07-21T21:22:24","slug":"neural-network-training-made-easy-with-smart-hardware","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2024\/07\/neural-network-training-made-easy-with-smart-hardware","title":{"rendered":"Neural network training made easy with smart hardware"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/neural-network-training-made-easy-with-smart-hardware2.jpg\"><\/a><\/p>\n<p>Large-scale neural network models form the basis of many AI-based technologies such as neuromorphic chips, which are inspired by the human brain. Training these networks can be tedious, time-consuming, and energy-inefficient given that the model is often first trained on a computer and then transferred to the chip. This limits the application and efficiency of neuromorphic chips.<\/p>\n<p>TU\/e researchers have solved this problem by developing a neuromorphic device capable of on\u2013<a href=\"https:\/\/techxplore.com\/tags\/chip\/\" rel=\"tag\" class=\"\">chip<\/a><a href=\"https:\/\/techxplore.com\/tags\/training\/\" rel=\"tag\" class=\"\">training<\/a> that eliminates the need to transfer trained models to the chip. This could open a route toward efficient and dedicated AI chips.<\/p>\n<p>Have you ever thought about how wonderful your brain really is? It\u2019s a powerful computing machine, but it\u2019s also fast, dynamic, adaptable, and very energy efficient.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Large-scale neural network models form the basis of many AI-based technologies such as neuromorphic chips, which are inspired by the human brain. Training these networks can be tedious, time-consuming, and energy-inefficient given that the model is often first trained on a computer and then transferred to the chip. This limits the application and efficiency of [\u2026]<\/p>\n","protected":false},"author":367,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-193272","post","type-post","status-publish","format-standard","hentry","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/193272","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\/367"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=193272"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/193272\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=193272"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=193272"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=193272"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}