{"id":132669,"date":"2021-12-18T19:22:21","date_gmt":"2021-12-19T03:22:21","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2021\/12\/optical-chip-promises-350x-speedup-over-rtx-3080-in-some-algorithms"},"modified":"2021-12-18T19:22:21","modified_gmt":"2021-12-19T03:22:21","slug":"optical-chip-promises-350x-speedup-over-rtx-3080-in-some-algorithms","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2021\/12\/optical-chip-promises-350x-speedup-over-rtx-3080-in-some-algorithms","title":{"rendered":"Optical Chip Promises 350x Speedup Over RTX 3080 in Some Algorithms"},"content":{"rendered":"<p><\/p>\n<p><iframe style=\"display: block; margin: 0 auto; width: 100%; aspect-ratio: 4\/3; object-fit: contain;\" src=\"https:\/\/www.youtube.com\/embed\/HEtyEJxLGIc?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope;\n   picture-in-picture\" allowfullscreen><\/iframe><\/p>\n<p>Lightelligence, a Boston-based photonics company, revealed the world\u2019s first small form-factor, photonics-based computing device, meaning it uses light to perform compute operations. The company claims the unit is \u201chundreds of times faster than a typical computing unit, such as NVIDIA RTX 3080.\u201d 350 times faster, to be exact, but that only applies to certain types of applications.<\/p>\n<hr>\n<p>However, the PACE achieves that coveted specialization through an added field of computing \u2014 which not only makes the system faster, it makes it incredibly more efficient. While traditional semiconductor systems have the issue of excess heat that results from running current through nanometre-level features <a href=\"https:\/\/www.tomshardware.com\/news\/fx-6300-breakes-8ghz\">at sometimes ludicrous frequencies<\/a>, the photonic system processes its workloads with zero Ohmic heating \u2014 there\u2019s no heat produced from current resistance. Instead, it\u2019s all about light.<\/p>\n<p>Lightelligence is built around its CEO\u2019s Ph.d. thesis \u2014 and the legitimacy it provides. This is so because when \u201cDeep Learning with Coherent Nanophotonic Circuits\u201d was published in Nature in 2017, Lightelligence\u2019s CEO and founder Yichen Chen had already foreseen a path for optical circuits to be at the forefront of Machine Learning computing efforts. By 2020, the company had already received $100 million in funding and employed around 150 employees. A year later, Lightspeed has achieved a dem product that it says is \u201chundreds of times faster than a typical computing unit, such as NVIDIA RTX 3080\u201d. 350 times faster, to be clear.<\/p>\n<p>The PACE\u2019s debut aims to charm enough capital to comfortably reach its goal of launching a pilot AI accelerator product to the market in 2022. That\u2019s still only a stretch goal in the company\u2019s vision, however, its goal is to develop and distribute a mass-market, photonics-based hardware solution as early as 2023, targeting the Cloud AI, Finance, and Retail markets. Considering how Lightelligence managed to improve the company\u2019s 2019 COMET design performance by a factor of a million with PACE in a span of two years, it\u2019ll be interesting to see where their efforts take them when it comes to launching.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Lightelligence, a Boston-based photonics company, revealed the world\u2019s first small form-factor, photonics-based computing device, meaning it uses light to perform compute operations. The company claims the unit is \u201chundreds of times faster than a typical computing unit, such as NVIDIA RTX 3080.\u201d 350 times faster, to be exact, but that only applies to certain types [\u2026]<\/p>\n","protected":false},"author":534,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[45,41,6,8],"tags":[],"class_list":["post-132669","post","type-post","status-publish","format-standard","hentry","category-finance","category-information-science","category-robotics-ai","category-space"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/132669","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\/534"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=132669"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/132669\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=132669"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=132669"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=132669"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}