In machine learning, traditional linear prediction techniques are well understood and methods for their efficient solution have been developed. Many real-world applications are better modelled using non-linear techniques, which often have high computational requirements. Kernel methods utilise linear methods in a non-linear feature space and combine the advantages of both. Commonly used kernel methods include the support vector machine (SVM), Gaussian processes and regularisation networks. These are batch-based, and a global optimisation is conducted over all input exemplars to create a model. In contrast, online methods, such as the kernel recursive least squares (KRLS) algorithm, update the state in a recursive and incremental fashion upon receiving a new exemplar. Although not as extensively studied as batch methods, online approaches are advantageous when throughput and latency are critical. In this talk I will describe efforts in the Computer Engineering Laboratory to produce high-performance FPGA-based implementations of online kernel methods. These have included: (1) a microcoded vector processor optimised for kernel methods; (2) a fully pipelined implementation of kernel normalised least mean squares which achieves 160 GFLOPS; (3) an implementation of Naive Online regularised Risk Minimization Algorithm (NORMA) which uses “braiding” to resolve data hazards and reduce latency by an order of magnitude; and (4) a distributed kernel recursive least squares algorithm which constructs a compact model while enabling massive parallelism.<\/p>\n","protected":false},"excerpt":{"rendered":"
In machine learning, traditional linear prediction techniques are well understood and methods for their efficient solution have been developed. Many real-world applications are better modelled using non-linear techniques, which often have high computational requirements. Kernel methods utilise linear methods in a non-linear feature space and combine the advantages of both. Commonly used kernel methods include […]<\/p>\n","protected":false},"featured_media":317591,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[13556],"msr-video-type":[206954],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-317531","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-video-type-microsoft-research-talks","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/thKUe0dmKGg","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/317531","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/317531\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/317591"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=317531"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=317531"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=317531"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=317531"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=317531"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=317531"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=317531"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=317531"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=317531"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=317531"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}