small point: mshadow is being deprecated. probably you shouldn’t invest too much time on it. just an FYI
On Sun, Dec 16, 2018 at 6:33 PM 张昊翀 <zhanghaoch...@cambricon.com> 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 > > >
