{"action":"created","issue":{"url":"https://api.github.com/repos/andravin/wincnn/issues/18","repository_url":"https://api.github.com/repos/andravin/wincnn","labels_url":"https://api.github.com/repos/andravin/wincnn/issues/18/labels{/name}","comments_url":"https://api.github.com/repos/andravin/wincnn/issues/18/comments","events_url":"https://api.github.com/repos/andravin/wincnn/issues/18/events","html_url":"https://github.com/andravin/wincnn/issues/18","id":388086288,"node_id":"MDU6SXNzdWUzODgwODYyODg=","number":18,"title":"How to calculate the transform matrixes for F(MxN, 5x3) formal.","user":{"login":"lujnan","id":6070444,"node_id":"MDQ6VXNlcjYwNzA0NDQ=","avatar_url":"https://avatars0.githubusercontent.com/u/6070444?v=4","gravatar_id":"","url":"https://api.github.com/users/lujnan","html_url":"https://github.com/lujnan","followers_url":"https://api.github.com/users/lujnan/followers","following_url":"https://api.github.com/users/lujnan/following{/other_user}","gists_url":"https://api.github.com/users/lujnan/gists{/gist_id}","starred_url":"https://api.github.com/users/lujnan/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lujnan/subscriptions","organizations_url":"https://api.github.com/users/lujnan/orgs","repos_url":"https://api.github.com/users/lujnan/repos","events_url":"https://api.github.com/users/lujnan/events{/privacy}","received_events_url":"https://api.github.com/users/lujnan/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2018-12-06T07:19:06Z","updated_at":"2018-12-06T20:59:42Z","closed_at":null,"author_association":"NONE","body":""},"comment":{"url":"https://api.github.com/repos/andravin/wincnn/issues/comments/445027629","html_url":"https://github.com/andravin/wincnn/issues/18#issuecomment-445027629","issue_url":"https://api.github.com/repos/andravin/wincnn/issues/18","id":445027629,"node_id":"MDEyOklzc3VlQ29tbWVudDQ0NTAyNzYyOQ==","user":{"login":"andravin","id":8460517,"node_id":"MDQ6VXNlcjg0NjA1MTc=","avatar_url":"https://avatars0.githubusercontent.com/u/8460517?v=4","gravatar_id":"","url":"https://api.github.com/users/andravin","html_url":"https://github.com/andravin","followers_url":"https://api.github.com/users/andravin/followers","following_url":"https://api.github.com/users/andravin/following{/other_user}","gists_url":"https://api.github.com/users/andravin/gists{/gist_id}","starred_url":"https://api.github.com/users/andravin/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/andravin/subscriptions","organizations_url":"https://api.github.com/users/andravin/orgs","repos_url":"https://api.github.com/users/andravin/repos","events_url":"https://api.github.com/users/andravin/events{/privacy}","received_events_url":"https://api.github.com/users/andravin/received_events","type":"User","site_admin":false},"created_at":"2018-12-06T20:59:42Z","updated_at":"2018-12-06T20:59:42Z","author_association":"OWNER","body":"I should also add that larger tile sizes are not always faster in practice. If your application requires a small input size (say batch size N=1, and small feature map Height and Width), or the size of your processor's caches is small (as on a typical GPU), a smaller tile might achieve much higher computational efficiency.\r\n\r\nIn the small data / small cache regime, it is worth examining the possibility that you leave one dimension of the convolution as a direct convolution. This gives you high computational intensity and small tile size. This approach might especially make sense in a hardware accelerator where MACs are cheap but memory extremely limited."}}
{"actor":{"display_login":"andravin"}}