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
