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Crowdsourcing benchmarking, optimization and co-design of deep learning
This is a prototype of our open-source client based on the open-source Collective Knowledge framework (https://github.com/ctuning/ck) to help the community co-design Pareto-efficient software and hardware stack for deep learning and other emerging workloads in terms of speed, accuracy, energy and costs across diverse models, data sets, libraries, frameworks and devices provided provided by volunteers similar to SETI@HOME:
* http://cKnowledge.org/dnn-crowd-benchmarking-results
* http://cKnowledge.org/request
* http://cKnowledge.org/ai
* https://github.com/ctuning/ck
License: permissive 3-clause BSD
Source code: https://github.com/dividiti/crowdsource-video-experiments-on-android
Maintainers: non-profit cTuning foundation (cTuning.org) and dividiti (dividiti.com)
Novità nell'ultima versione 2.12
Last updated on Aug 1, 2018
Added ArmCL 18.05 OpenCL library with 3 MobileNets v1 scenario; updated scoreboard at http://cKnowledge.org/dnn-crowd-benchmarking-results (sorting by throughput and latency)
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Crowd-benchmarking and crowd-tuning deep learning
2.12 by dividiti Ltd
Aug 1, 2018