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The hunt for a way to meet artificial intelligence<\/a>\u2018s demand for energy has made its way to Clemson University, where a team has created a new material aimed at transforming how computers process and store information.<\/p>\n The new material, pTPADTP, is a polymer that could be key to making AI more energy efficient and cost effective, Stephen Foulger<\/a>, Clemson\u2019s Gregg-Graniteville Endowed Chair and professor, said in a news release.<\/p>\n \u201cThe energy costs are not trivial, and I don\u2019t think people understand how severe the energy costs are,\u201d he said in the release. \u201cBreaking the standard technology we have right now into something new to support AI and the growth of AI is what this material is all about.\u201d<\/p>\n Electricity demand from data centers \u2014 the physical hubs of AI \u2014 is on course to more than double worldwide by 2030 to about 945 terawatt-hours, slightly more than Japan\u2019s electricity consumption, according to an April report from the\u00a0International Energy Agency<\/a>.<\/p>\n Options for helping meet the demand have ranged from restarting nuclear reactors to sinking data centers in the ocean to cool them. The new material developed at Clemson adds an innovative and potentially transformative approach, the university says.<\/p>\n Foulger, who led the development of the material, said it could upend how computers have worked since the 1950s.<\/p>\n In classical computers, memory and processing are separate. One part of the chip stores the data, and another part processes it. The computer needs to move data back and forth between these two parts, which slows things down and eats up energy.<\/p>\n But pTPADTP could be used to build a memristor<\/a> \u2014 a device that can both store and process information in the same spot. That means less data shuttling, cutting down on energy use, the release stated.<\/p>\n What sets the Clemson team\u2019s work apart is its focus on probabilistic bits, or p-bits, a middle ground between the classical bits used in today\u2019s computers and the quantum bits that power quantum computing.<\/p>\n Unlike regular bits, which are either 0 or 1, p-bits flip randomly between 0 and 1, but in a controlled way. This randomness is valuable for certain types of computing, such as running AI models.<\/p>\n The pTPADTP material developed at Clemson allows for p-bit behavior using a polymer, which is cheaper and easier to work with than traditional materials such as magnetic tunnel junctions. By creating these switches, the research opens the door to new kinds of energy-efficient computing systems that embrace randomness as a tool rather than a flaw.<\/p>\n pTPADTP is an acronym for poly-4-((6-(4H-dithieno[3,2-b:2\u2032,3\u2032-d]pyrrol-4-yl)hexyl)oxy)-N,N-diphenylaniline.<\/p>\n Kyle Brinkman<\/a>, chair of the\u00a0Department of Materials Science and Engineering<\/a>, said the team\u2019s work is a creative way of addressing one of the most challenging and urgent topics in artificial intelligence.<\/p>\n \u201cThis work shines a spotlight on the crucial role materials science<\/a> and engineering plays in advancing AI while underscoring the innovative ways we tackle real-world challenges in the department,\u201d he said in the release. \u201cI congratulate Dr. Foulger and his team on publishing their findings.\u201d<\/p>\n The team\u2019s work was published in the journal Advanced Physics Research. The paper\u2019s title was \u201cPolymeric Memristors as Entropy Sources for Probabilistic Bit Generation<\/a>.\u201d<\/p>\n In addition to Foulger, the co-authors were Yuriy Bandera,\u00a0Igor Luzinov<\/a>, Travis Wanless, all of Clemsons\u2019\u00a0Center for Optical Materials Science and Engineering Technologies (COMSET)<\/a>.<\/p>\n C<\/p>\n","protected":false},"excerpt":{"rendered":"Listen to this article \u200b Clemson University develops pTPADTP polymer to reduce AI energy use New material enables…\n","protected":false},"author":2,"featured_media":156797,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3164],"tags":[64635,66523,1942,66524,3284,66525,22098,66526,66527,66528,66529,53,16,15],"class_list":{"0":"post-156796","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-computing","8":"tag-advanced-computing","9":"tag-ai-energy-use","10":"tag-artificial-intelligence","11":"tag-clemson-university","12":"tag-computing","13":"tag-data-center-energy","14":"tag-materials-science","15":"tag-memristor","16":"tag-p-bits","17":"tag-polymer-research","18":"tag-ptpadtp","19":"tag-technology","20":"tag-uk","21":"tag-united-kingdom"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@uk\/114623707038432239","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/156796","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/comments?post=156796"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/156796\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media\/156797"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media?parent=156796"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/categories?post=156796"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/tags?post=156796"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}