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From Hagay Lupesko <>
Subject Re: Cambricon MLU support for MXNet.
Date Mon, 17 Dec 2018 06:03:07 GMT
Welcome to the MXNet community Haochong!
It's exciting to learn about your plans to contribute to MXNet!

I highly recommend that you document your proposal and technical design in
MXNet's design proposals wiki [1
where you can go into details and ask for comprehensive feedback from the



On Sun, Dec 16, 2018 at 6:33 PM 张昊翀 <> wrote:

> Dear MXNet community,
> We are from Cambricon, a leading supplier of artificial intelligence
> chips. We have two product lines, including IP products (e.g., Cambricon
> 1A/1H) and chip products (e.g., MLU100 released in May 2018)
> We are now adapting MXNet on Cambricon products. During the follow-up
> session, we plan to open source, and hope to merge these new features into
> the master branch of MXNet and to be a part of MXNet's long-term support.
> We firmly believe that these MLU features will promote the MXNet community
> development.
> To this end, we are ready to accept the rigorous inspection of MXNet
> community. In addition, we need advice from the community to achieve high
> quality implementation. On this basis, we very much hope to reach a
> full-scale long-term cooperation with the community.
> In order to achieve the above goals, we hope to keep in touch with the
> community on some issues. Looking forward to your valuable feedback.
> 1. MLU100 mainly focuses on inference, and we plan to first support the
> inference part of MXNet. The training part of MXNet on MLU will be released
> in the future. Is that acceptable for MXNet community?
> 2. Though MLU can support various operators/networks, to guarantee high
> quality, all supported operators submitted to the community should undergo
> rigorous stress test. Thus, at the beginning, we plan to release a small
> number of supported operators and networks, and more of them will be
> continuously added. Is that acceptable or do we have to support all
> networks in the ModelZoo in the first release?
> 3. Currently we plan to support both Python and C++ APIs. More details on
> supported APIs will be provided in a follow-up proposal.
> 4. We need to modify the mShadow in order to support tensor memory
> operations.
> 5. In order to enable the community to run and fully test our code, we
> want to provide the community with a complete test environment. At present,
> we are considering the following three ways.
> A) Provides several remote servers for community and integrates with the
> community's Jenkins.
> B) Provide a cloud platform to the community.
> C) Donate MLU100 to the community's testing platform. However, we don’t
> know the specific ways of donation, and we hope to get help. We are
> wondering about how MXNet's test servers are managed.
> About more technical details, a proposal will be submitted to the
> community before releasing the code.
> In addition to the above points, the remaining questions and suggestions
> are also welcome. Thanks!
> More about Cambricon:
> Cambricon is the artificial intelligence computing pioneer that engineers
> and successfully commercializes world’s first dedicated machine learning
> processor. To bring its unique AI processors from edge to cloud, enriching
> and advancing human life, is the firm mission of the company. Dr. Tianshi
> Chen is the founder and CEO of Cambricon, where he brings over 10 years
> experience in the fields of micro-processor architecture and artificial
> intelligence.
> In 2016, Cambricon released Cambricon 1A processor, the first commercial
> machine learning specific processor in the world. Later, during the 3rd
> World Internet Conference, Cambricon 1A processor was elected as one of
> “World Leading Internet Scientific and Technological Achievements“. In May
> 2018, Cambricon released MLU100, a machine learning chip which is in mass
> production now. By offering revolutionary technology and products,
> Cambricon has established and remains active relationships with various
> companies in the AI industry.
> Regards,
> Haochong Zhang
> Cambricon MXNet Development Team

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