Reference number : GC-2017-Q1-01424, amendment number : , agreement type : G, recipient legal name : Moshovos, Andreas (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : DND/NSERC Discovery Grant Supplement, program name french : Supplément aux subventions à la découverte MDN-CRSNG, program purpose english : Over the past few years we have been witnessing unprecedented advances on what computing can do for us. It is suddenly possible to reliably talk to our phones, or to look up information. Online translation between languages have become very reliable and useful. There are vehicles that can drive themselves with very little human supervision, and there are constant news stories about how computers are being put to test to help with many difficult tasks such as medical diagnosis. The one technology behind all these innovations is Deep Learning , a class of computing algorithms that allow computers to learn on their own given enough examples. The core technology behind Deep Learning has been around for decades. However, it is only recently that access to vast amounts of online information became available and computing hardware performance has reached levels that allowed the first practical applications of Deep Learning. While existing hardware was sufficient for these first breakthroughs in Deep Learning, it is not capable enough to sustain innovation. Even worse, while in the past computing hardware performance was improving predictably over the years due to advances in semiconductor technology, this is no longer possible due to fundamental challenges in this technology. One way to deliver the hardware performance increases that will enable further innovation in Deep Learning is to build specialized hardware. This five year research program will investigate such specialized hardware designs enabling Deep Learning researchers to further innovate and to bring us closer to truly intelligent machines. The core concept behind our specialized hardware is that it takes advantage of the values calculated upon during Deep Learning processing. The result will be a programmable, yet specialized architecture that will deliver at least three orders of performance improvements over commodity solutions and that can be tailored to various use scenarios from large scale installations such as data centers to mobile and embedded devices., program purpose french : Over the past few years we have been witnessing unprecedented advances on what computing can do for us. It is suddenly possible to reliably talk to our phones, or to look up information. Online translation between languages have become very reliable and useful. There are vehicles that can drive themselves with very little human supervision, and there are constant news stories about how computers are being put to test to help with many difficult tasks such as medical diagnosis. The one technology behind all these innovations is Deep Learning , a class of computing algorithms that allow computers to learn on their own given enough examples. The core technology behind Deep Learning has been around for decades. However, it is only recently that access to vast amounts of online information became available and computing hardware performance has reached levels that allowed the first practical applications of Deep Learning. While existing hardware was sufficient for these first breakthroughs in Deep Learning, it is not capable enough to sustain innovation. Even worse, while in the past computing hardware performance was improving predictably over the years due to advances in semiconductor technology, this is no longer possible due to fundamental challenges in this technology. One way to deliver the hardware performance increases that will enable further innovation in Deep Learning is to build specialized hardware. This five year research program will investigate such specialized hardware designs enabling Deep Learning researchers to further innovate and to bring us closer to truly intelligent machines. The core concept behind our specialized hardware is that it takes advantage of the values calculated upon during Deep Learning processing. The result will be a programmable, yet specialized architecture that will deliver at least three orders of performance improvements over commodity solutions and that can be tailored to various use scenarios from large scale installations such as data centers to mobile and embedded devices., agreement title english : Deep Learning Hardware: Enabling the next wave of applications and innovation, agreement title french : Deep Learning Hardware: Enabling the next wave of applications and innovation, agreement number : DGDND, agreement value : 120000.0, agreement start date : 2017-06-14, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2020-2021), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2020-2021), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada