{"id":2206,"date":"2017-10-19T07:56:22","date_gmt":"2017-10-19T07:56:22","guid":{"rendered":"http:\/\/nvmw.ucsd.edu\/2018-archive\/?p=2206"},"modified":"2017-10-19T07:57:53","modified_gmt":"2017-10-19T07:57:53","slug":"the-n3xt-technology-for-brain-inspired-computing","status":"publish","type":"post","link":"https:\/\/nvmw.ucsd.edu\/2018\/2017\/10\/19\/the-n3xt-technology-for-brain-inspired-computing\/","title":{"rendered":"The N3XT technology for Brain Inspired Computing"},"content":{"rendered":"
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The N3XT technology for Brain Inspired Computing<\/h3>\n\r\n\t\t<\/div>\r\n\t<\/div>\r\n<\/div><\/div><\/div><\/div>
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Abstract:<\/strong> Advances in brain-inspired computing are making rapid progress to meet the demands of abundant-data processing using a variety of techniques, including spiking neural networks, hyperdimensional computing using sparse vectors, deep neural nets, deep belief nets, restricted Boltzmann machines, and their variants. It is therefore crucial to create a scalable and flexible brain-inspired technology platform that can support all the essential elements, and can be adapted for a wide variety of neural computational model.<\/p>\n

The key elements of a scalable, fast, and energy-efficient computation platform that may provide another 1,000\u00d7 in computing performance (energy-execution time product) for future computing workloads are: massive on-chip memory co-located with highly energy-efficient computation, enabled by monolithic 3D integration using ultra-dense and fine-grained massive connectivity. There will be multiple layers of analog and digital memories interleaved with computing logic, sensors, and application-specific devices. We call this technology platform N3XT \u2013 Nanoengineered Computing Systems Technology. N3XT will support computing architectures that embrace sparsity, stochasticity, and device variability.<\/p>\n

In this talk, I will give an overview of nanoscale memory and logic technologies for implementing N3XT. In particular, I give an overview of the use of nanoscale analog non-volatile memory devices for implementing brain-inspired computing. Phase change memory (PCM) and resistive switching memory (RRAM) are used as examples to illustrate the need to co-design, co-optimize the device technology, circuit design, system architecture, and learning algorithms.<\/p>\n

Bio:<\/strong> H.-S. Philip Wong is the Willard R. and Inez Kerr Bell Professor in the School of Engineering. He joined Stanford University as Professor of Electrical Engineering in September, 2004. From 1988 to 2004, he was with the IBM T.J. Watson Research Center.
\nAt IBM, he held various positions from Research Staff Member to Manager and Senior Manager. While he was Senior Manager, he had the responsibility of shaping and executing IBM\u2019s strategy on nanoscale science and technology as well as exploratory silicon devices and semiconductor technology.<\/p>\n

Professor Wong\u2019s research aims at translating discoveries in science into practical technologies. His works have contributed to advancements in nanoscale science and technology, semiconductor technology, solid-state devices, and electronic imaging.<\/p>\n

He is a Fellow of the IEEE. He served as the Editor-in-Chief of the IEEE Transactions on Nanotechnology in 2005 \u2013 2006, sub-committee Chair of the ISSCC (2003 \u2013 2004), General Chair of the IEDM (2007), and is currently the Chair of the IEEE Executive Committee of the Symposia of VLSI Technology and Circuits. He is the founding Faculty Co-Director of the Stanford SystemX Alliance \u2013 an industrial affiliate program focused on building systems.<\/p>\n\r\n\t\t<\/div>\r\n\t<\/div>\r\n

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    \n\t<\/i>\n<\/div><\/div><\/div>\n<\/div>Watch the video.<\/span><\/div><\/li>
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    \n\t<\/i>\n<\/div><\/div><\/div>\n<\/div>Download the paper.<\/span><\/div><\/li>
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    \n\t<\/i>\n<\/div><\/div><\/div>\n<\/div>Download the presentation.<\/span><\/div><\/li><\/ul><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"The N3XT technology for Brain Inspired Computing Abstract: Advances in brain-inspired computing are making rapid progress to meet the demands of abundant-data processing using a variety of techniques, including spiking neural networks, hyperdimensional computing using sparse vectors, deep neural nets, deep belief nets, restricted Boltzmann machines, and their variants. It is therefore crucial to create...","protected":false},"author":1,"featured_media":1744,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"footnotes":""},"categories":[10],"tags":[],"class_list":["post-2206","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-program1"],"_links":{"self":[{"href":"https:\/\/nvmw.ucsd.edu\/2018\/wp-json\/wp\/v2\/posts\/2206","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nvmw.ucsd.edu\/2018\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nvmw.ucsd.edu\/2018\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nvmw.ucsd.edu\/2018\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/nvmw.ucsd.edu\/2018\/wp-json\/wp\/v2\/comments?post=2206"}],"version-history":[{"count":1,"href":"https:\/\/nvmw.ucsd.edu\/2018\/wp-json\/wp\/v2\/posts\/2206\/revisions"}],"predecessor-version":[{"id":2207,"href":"https:\/\/nvmw.ucsd.edu\/2018\/wp-json\/wp\/v2\/posts\/2206\/revisions\/2207"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nvmw.ucsd.edu\/2018\/wp-json\/wp\/v2\/media\/1744"}],"wp:attachment":[{"href":"https:\/\/nvmw.ucsd.edu\/2018\/wp-json\/wp\/v2\/media?parent=2206"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nvmw.ucsd.edu\/2018\/wp-json\/wp\/v2\/categories?post=2206"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nvmw.ucsd.edu\/2018\/wp-json\/wp\/v2\/tags?post=2206"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}