{"id":2057,"date":"2024-06-26T08:47:52","date_gmt":"2024-06-26T08:47:52","guid":{"rendered":"https:\/\/www.tun.com\/home\/?p=2057"},"modified":"2024-10-16T21:04:08","modified_gmt":"2024-10-16T21:04:08","slug":"researchers-unveil-breakthrough-in-energy-efficient-brain-inspired-computing","status":"publish","type":"post","link":"https:\/\/www.tun.com\/home\/researchers-unveil-breakthrough-in-energy-efficient-brain-inspired-computing\/","title":{"rendered":"Researchers Unveil Breakthrough in Energy-Efficient Brain-Inspired Computing"},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-uagb-blockquote uagb-block-e7eb3fc3 uagb-blockquote__skin-border uagb-blockquote__stack-img-none\"><blockquote class=\"uagb-blockquote\"><div class=\"uagb-blockquote__content\">A team of researchers from UC Santa Barbara and Intel Labs has proposed a groundbreaking platform for neuromorphic computing, significantly enhancing energy efficiency and potentially transforming the future of AI and IoT technologies.<\/div><footer><div class=\"uagb-blockquote__author-wrap uagb-blockquote__author-at-left\"><\/div><\/footer><\/blockquote><\/div>\n\n\n\n<div class=\"wp-block-group is-content-justification-space-between is-nowrap is-layout-flex wp-container-core-group-is-layout-b0ffac9c wp-block-group-is-layout-flex\"><div style=\"font-size:16px\" class=\"has-text-align-left wp-block-post-author\"><div class=\"wp-block-post-author__content\"><p class=\"wp-block-post-author__name\">The University Network<\/p><\/div><\/div>\n\n\n<div class=\"wp-block-uagb-social-share uagb-social-share__outer-wrap uagb-social-share__layout-horizontal uagb-block-ee584a31\">\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-ec619ce7\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.facebook.com\/sharer.php?u=\" tabindex=\"0\" role=\"button\" aria-label=\"facebook\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.8 90.69 226.4 209.3 245V327.7h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.3 482.4 504 379.8 504 256z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-32d99934\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/twitter.com\/share?url=\" tabindex=\"0\" role=\"button\" aria-label=\"twitter\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M389.2 48h70.6L305.6 224.2 487 464H345L233.7 318.6 106.5 464H35.8L200.7 275.5 26.8 48H172.4L272.9 180.9 389.2 48zM364.4 421.8h39.1L151.1 88h-42L364.4 421.8z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-1d136f14\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.linkedin.com\/shareArticle?url=\" tabindex=\"0\" role=\"button\" aria-label=\"linkedin\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Computers have made remarkable strides in processing power, prediction capabilities and data communication, often surpassing human capabilities. Yet, in one critical area, they lag significantly behind the human brain \u2014 energy efficiency.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cThe most efficient computers are still approximately four orders of magnitude \u2014 that\u2019s 10,000 times \u2014 higher in energy requirements compared to the human brain for specific tasks such as image processing and recognition,\u201d Kaustav Banerjee, a professor of electrical and computer engineering at UC Santa Barbara and an authority in nanoelectronics, said in a <a href=\"https:\/\/news.ucsb.edu\/2024\/021528\/researchers-propose-next-platform-brain-inspired-computing\" title=\"\">news release<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Banerjee emphasizes the importance of developing more energy-efficient computing technologies, especially given global energy consumption trends. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">&#8220;Making computers more energy efficient is crucial because the worldwide energy consumption by on-chip electronics stands at #4 in the global rankings of nation-wise energy consumption, and it is increasing exponentially each year, fueled by applications such as artificial intelligence,&#8221; he added. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The urgency of this challenge is magnified by contemporary concerns such as global warming.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Enter neuromorphic (NM) computing \u2014 a promising avenue for addressing this energy efficiency gap. By mimicking the brain&#8217;s structure and functionality, where processing happens in parallel across numerous low-power neurons, NM computing can potentially match the brain&#8217;s energy efficiency.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Recent advancements were showcased in a <a href=\"https:\/\/www.nature.com\/articles\/s41467-024-46397-3\" title=\"\">paper<\/a> published in Nature Communications. Banerjee and his colleagues Arnab Pal, Zichun Chai, Junkai Jiang and Wei Cao, along with Intel Labs&#8217; researchers Vivek De and Mike Davies, propose a revolutionary ultra-energy-efficient platform. This platform utilizes 2D transition metal dichalcogenide (TMD)-based tunnel-field-effect transistors (TFETs), bringing energy requirements to within two orders of magnitude (about 100 times) of the human brain\u2019s.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Neuromorphic Computing in Focus<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Neuromorphic computing has long been a theoretical concept but is now gaining traction thanks to recent progress in circuitry, which has enabled smaller and more efficient transistor arrays. This development is crucial for applications like artificial intelligence and the Internet of Things, driving the need for advanced hardware platforms.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Banerjee\u2019s team is at the forefront of this evolution with their 2D tunnel-transistors. These atomically thin, nanoscale transistors, developed through extensive research efforts, aim to achieve high performance while consuming minimal power. These transistors, which respond at low voltages, are foundational to the researchers\u2019 NM platform and can emulate the brain&#8217;s energy-efficient operations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The research indicates that these 2D TFETs reduce off-state currents and have a low subthreshold swing (SS), a measure of how efficiently transistors can switch between on and off states. Banerjee highlights that lower SS translates to lower operating voltage and more efficient switching.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cNeuromorphic computing architectures are designed to operate with very sparse firing circuits, meaning they mimic how neurons in the brain fire only when necessary,\u201d lead author Arnab Pal said in the news release. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This method contrasts with conventional computers, which continuously draw power and process data sequentially. Neuromorphic systems activate only when there is data to process, distributing memory and processing across transistors and significantly improving energy efficiency.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">While companies like Intel and IBM have developed brain-inspired platforms, these still lose significant energy through leakage currents in the transistors&#8217; off-state. Traditional metal-oxide-semiconductor field-effect transistors (MOSFETs) used in current NM chips have high off-state leakage. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cSince the power efficiency of these chips is constrained by the off-state leakage, our approach \u2014 using tunneling transistors with much lower off-state current \u2014 can greatly improve power efficiency,\u201d added Banerjee.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When integrated into neuromorphic circuits mimicking neuron firing and reset, TFETs outperformed state-of-the-art MOSFETs, particularly FinFETs, in energy efficiency. Although TFETs are still experimental, their demonstrated performance in NM circuits identifies them as promising candidates for the next generation of brain-inspired computing.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Computers have made remarkable strides in processing power, prediction capabilities and data communication, often surpassing human capabilities. Yet, in one critical area, they lag significantly behind the human brain \u2014 energy efficiency. \u201cThe most efficient computers are still approximately four orders of magnitude \u2014 that\u2019s 10,000 times \u2014 higher in energy requirements compared to the [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-no-separators","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[10],"tags":[],"class_list":["post-2057","post","type-post","status-publish","format-standard","hentry","category-sustainability"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.8 - aioseo.com -->\n\t<meta name=\"description\" content=\"Discover how UC Santa Barbara and Intel researchers are creating a new platform for ultra-energy-efficient neuromorphic computing, potentially revolutionizing technology by mimicking the human brain.\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"The University Network\"\/>\n\t<link rel=\"canonical\" href=\"https:\/\/www.tun.com\/home\/researchers-unveil-breakthrough-in-energy-efficient-brain-inspired-computing\/\" \/>\n\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 4.9.8\" \/>\n\t\t<meta property=\"og:locale\" content=\"en_US\" \/>\n\t\t<meta property=\"og:site_name\" content=\"TUN - 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