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क्राउडसोर्सिंग बेंच मार्किंग, अनुकूलन और गहरी सीखने के सह डिजाइन
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)
नवीनतम संस्करण 2.12 में नया क्या है
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)
अनुवाद लोड हो रहा है...
Crowd-benchmarking and crowd-tuning deep learning
2.12 by dividiti Ltd
Aug 1, 2018