Cambricon MLU support for MXNet.

[张昊翀 <zh...@cambricon.com>]

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

Re: Cambricon MLU support for MXNet.

[Steffen Rochel <st...@gmail.com>]

Hi Haochong -
welcome to MXNet. I invited you to the Slack channel, which you can use as
another communication path.

Regards,
Steffen

On Tue, Dec 18, 2018 at 5:45 AM Haochong Zhang <zh...@cambricon.com>
wrote:

> Thank you very much for your valuable feedback!
>
> We will submit the design proposal ASAP. At the same time, we will be
> ready for the appropriate server or cloud.
>
> The cambricon libraries related to MXNet are Cambricon Neuware Machine
> Learning Library (CNML) and Cambricon Neuware Runtime Library (CNRT). The
> libraries' documentation will be available soon.
>
> Look forward to continued participation and contribution in the future.
>
>
> ------------------------------------------------------------------
> 发件人：Skalicky, Sam <ss...@amazon.com>
> 发送时间：2018年12月18日(星期二) 06:03
> 收件人：dev@mxnet.incubator.apache.org <de...@mxnet.incubator.apache.org>
> 抄 送：张昊翀 <zh...@cambricon.com>
> 主 题：Re: Cambricon MLU support for MXNet.
>
> Hi Haochong,
>
> I am in the process of putting together a design proposal for an
> accelerator interface for MXNet that would allow hardware vendors to
> integrate their runtime with MXNet. I would like to suggest setting up a
> time to get together so that we can hear more about your needs to
> interface/control your accelerator, and I can share some thought on a
> generic accelerator API that I will be proposing. Id be happy to help you
> prepare a design proposal as well.
>
> I’ll connect with you separately to setup a time to chat.
>
> Sam
>
>
> > On Dec 17, 2018, at 5:49 AM, Pedro Larroy <pe...@gmail.com>
> wrote:
> >
> > Hi Haochong
> >
> > Welcome to MXNet, It's exciting to have additional hardware platforms
> > added and supported in the project.
> >
> > The CI system for MXNet is donated by AWS to the project. We have a
> > small hardware lab with embedded physical hardware like ARM boards
> > including NVidia Jetson which we are connecting to the CI system.
> > (It's a WIP).
> >
> > However, the bulk of the CI system runs in the AWS Cloud using Jenkins
> > and EC2 GPU and CPU instances. So even though any of the options you
> > mention are possible and could work, I think in the order you
> > mentioned them would be the most preferable. Connecting a remote
> > server or cloud instance to the MXNet Jenkins would be the easiest
> > which wouldn't involve hardware shipping and maintenance.
> >
> > I think once you have the contribution merged and the changes ready to
> > be tested we can make a plan on how to best integrate with CI. For
> > that, the recommendation that Hagay gave (Design proposal in the Wiki)
> > is a good path forward, so other members of the community and the
> > engineers contributing to the CI system can contribute.
> >
> > Pedro.
> >
> > On Mon, Dec 17, 2018 at 3:33 AM 张昊翀 <zh...@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
> >>
> >>
>
>
>

回复：Cambricon MLU support for MXNet.

[Haochong Zhang <zh...@cambricon.com>]

Thank you very much for your valuable feedback!

We will submit the design proposal ASAP. At the same time, we will be ready for the appropriate server or cloud. 

The cambricon libraries related to MXNet are Cambricon Neuware Machine Learning Library (CNML) and Cambricon Neuware Runtime Library (CNRT). The libraries' documentation will be available soon.

Look forward to continued participation and contribution in the future.


------------------------------------------------------------------
发件人：Skalicky, Sam <ss...@amazon.com>
发送时间：2018年12月18日(星期二) 06:03
收件人：dev@mxnet.incubator.apache.org <de...@mxnet.incubator.apache.org>
抄　送：张昊翀 <zh...@cambricon.com>
主　题：Re: Cambricon MLU support for MXNet.

Hi Haochong,

I am in the process of putting together a design proposal for an accelerator interface for MXNet that would allow hardware vendors to integrate their runtime with MXNet. I would like to suggest setting up a time to get together so that we can hear more about your needs to interface/control your accelerator, and I can share some thought on a generic accelerator API that I will be proposing. Id be happy to help you prepare a design proposal as well. 

I’ll connect with you separately to setup a time to chat. 

Sam


> On Dec 17, 2018, at 5:49 AM, Pedro Larroy <pe...@gmail.com> wrote:
> 
> Hi Haochong
> 
> Welcome to MXNet, It's exciting to have additional hardware platforms
> added and supported in the project.
> 
> The CI system for MXNet is donated by AWS to the project. We have a
> small hardware lab with embedded physical hardware like ARM boards
> including NVidia Jetson which we are connecting to the CI system.
> (It's a WIP).
> 
> However, the bulk of the CI system runs in the AWS Cloud using Jenkins
> and EC2 GPU and CPU instances. So even though any of the options you
> mention are possible and could work, I think in the order you
> mentioned them would be the most preferable. Connecting a remote
> server or cloud instance to the MXNet Jenkins would be the easiest
> which wouldn't involve hardware shipping and maintenance.
> 
> I think once you have the contribution merged and the changes ready to
> be tested we can make a plan on how to best integrate with CI. For
> that, the recommendation that Hagay gave (Design proposal in the Wiki)
> is a good path forward, so other members of the community and the
> engineers contributing to the CI system can contribute.
> 
> Pedro.
> 
> On Mon, Dec 17, 2018 at 3:33 AM 张昊翀 <zh...@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
>> 
>> 

Re: Cambricon MLU support for MXNet.

[Pedro Larroy <pe...@gmail.com>]

Hi Haochong

Welcome to MXNet, It's exciting to have additional hardware platforms
added and supported in the project.

The CI system for MXNet is donated by AWS to the project. We have a
small hardware lab with embedded physical hardware like ARM boards
including NVidia Jetson which we are connecting to the CI system.
(It's a WIP).

However, the bulk of the CI system runs in the AWS Cloud using Jenkins
and EC2 GPU and CPU instances. So even though any of the options you
mention are possible and could work, I think in the order you
mentioned them would be the most preferable. Connecting a remote
server or cloud instance to the MXNet Jenkins would be the easiest
which wouldn't involve hardware shipping and maintenance.

I think once you have the contribution merged and the changes ready to
be tested we can make a plan on how to best integrate with CI. For
that, the recommendation that Hagay gave (Design proposal in the Wiki)
is a good path forward, so other members of the community and the
engineers contributing to the CI system can contribute.

Pedro.

On Mon, Dec 17, 2018 at 3:33 AM 张昊翀 <zh...@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
>
>

Re: Cambricon MLU support for MXNet.

[Hagay Lupesko <lu...@gmail.com>]

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
<https://cwiki.apache.org/confluence/display/MXNET/Design+Proposals>],
where you can go into details and ask for comprehensive feedback from the
community.

Cheers,
Hagay

[1] https://cwiki.apache.org/confluence/display/MXNET/Design+Proposals

On Sun, Dec 16, 2018 at 6:33 PM 张昊翀 <zh...@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
>
>
>

RE: Cambricon MLU support for MXNet.

["Lv, Tao A" <ta...@intel.com>]

"mshadow is being deprecated." 

Surprised to know that. Was it discussed before? Do we have any document to tell contributors and developers about that?

-tao

-----Original Message-----
From: Chris Olivier [mailto:cjolivier01@gmail.com] 
Sent: Monday, December 17, 2018 3:10 PM
To: dev@mxnet.incubator.apache.org; ??? <zh...@cambricon.com>
Cc: dev@mxnet.apache.org; solomon.zhc <so...@gmail.com>
Subject: Re: Cambricon MLU support for MXNet.

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 <zh...@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
>
>
>

Re: Cambricon MLU support for MXNet.

[Chris Olivier <cj...@gmail.com>]

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 <zh...@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
>
>
>