{"id":10775,"date":"2021-06-17T07:00:00","date_gmt":"2021-06-17T15:00:00","guid":{"rendered":"https:\/\/formtek.com\/blog\/?p=10775"},"modified":"2021-03-02T08:59:24","modified_gmt":"2021-03-02T16:59:24","slug":"artificial-intelligence-downsizing-to-8-bits-to-balance-power-and-performance","status":"publish","type":"post","link":"https:\/\/formtek.com\/blog\/artificial-intelligence-downsizing-to-8-bits-to-balance-power-and-performance\/","title":{"rendered":"Artificial Intelligence: Downsizing to 8-bits to Balance Power and Performance"},"content":{"rendered":"\n

The computing industry has progressed from 8-bit computers, to 16-bit, 32-bit, and then 64-bit. With each new generation, the computers were more performant and significantly faster.<\/p>\n\n\n\n

So it seems odd and counterintuitive that AI researchers now say 8-bit computers may be the key to speedier AI computations, especially when computing needs to be performed on small devices.<\/p>\n\n\n\n

IBM researchers found that<\/a> “historically, high-performance computing has relied on high precision 64 and 32-bit floating point arithmetic. This approach delivers accuracy critical for scientific computing tasks like simulating the human heart or calculating space shuttle trajectories. But do we need this level of accuracy for common perception and reasoning tasks such as speech recognition, image classification and language translation? The answer is that many of these tasks (accomplished today using deep learning) can be computed effectively with approximate techniques.”<\/p>\n\n\n\n

Chris Williams<\/a>, editor at the Register, wrote that<\/a> “8-bit precision is fine for neural networks, and that allows GPUs to shuttle around more bytes than they would if they were crunching wider 16-bit or 32-bit values. You don’t need that level of precision when rippling input data through deep levels of perceptrons.”<\/p>\n\n\n\n

Jeffrey Wesler<\/a>, VP of Research at IBM, said that<\/a> “for 32-bit calculation, I\u2019ve got to do calculation on 32-bits. If we can do it on 16 bits, that\u2019s basically half the calculation power, or probably half the area or even less on a chip. If you can get down to 8 bits or 4 bits, that\u2019s even better. So, this gives me a huge win for area, for power, and performance and throughput \u2014 how fast we can get through all of this.<\/p>\n\n\n\n

Wesler said that<\/a> “in-memory computing may be able to achieve high-performance deep learning in low-power environments, such as IoT and edge applications. As with our digital accelerators, our analog chips are designed to scale for AI training and inferencing across visual, speech, and text datasets and extend to emerging broad AI.”<\/p>\n

\"Digg<\/a><\/div>
\"Reddit<\/a><\/div>
\"Stumble<\/a><\/div>
\"Buzz<\/a><\/div>
\"Vote<\/a><\/div>
\"Share<\/a><\/div>
\"Bookmark<\/a><\/div>
\"Kick<\/a><\/div>
\"Shout<\/a><\/div>
\"Share<\/a><\/div>
\"Bookmark<\/a><\/div>
\"Post<\/a><\/div>
\"Google<\/a><\/div><\/div>","protected":false},"excerpt":{"rendered":"

The computing industry has progressed from 8-bit computers, to 16-bit, 32-bit, and then 64-bit. With each new generation, the computers were more performant and significantly faster. So it seems odd and counterintuitive that AI researchers now say 8-bit computers may…<\/span><\/p>\n

Read more ›<\/a><\/div>\n

<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[69],"tags":[],"class_list":["post-10775","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence"],"_links":{"self":[{"href":"https:\/\/formtek.com\/blog\/wp-json\/wp\/v2\/posts\/10775","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/formtek.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/formtek.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/formtek.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/formtek.com\/blog\/wp-json\/wp\/v2\/comments?post=10775"}],"version-history":[{"count":1,"href":"https:\/\/formtek.com\/blog\/wp-json\/wp\/v2\/posts\/10775\/revisions"}],"predecessor-version":[{"id":10776,"href":"https:\/\/formtek.com\/blog\/wp-json\/wp\/v2\/posts\/10775\/revisions\/10776"}],"wp:attachment":[{"href":"https:\/\/formtek.com\/blog\/wp-json\/wp\/v2\/media?parent=10775"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/formtek.com\/blog\/wp-json\/wp\/v2\/categories?post=10775"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/formtek.com\/blog\/wp-json\/wp\/v2\/tags?post=10775"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}