BrainChip + Riverside Research

[uiux]

https://www.iarpa.gov/images/PropsersDayPDFs/SCISRS/Riverside_Research_Capabilites.pdf

Riverside Research Capabilities to Support the IARPA Securing Compartmented Information with Smart Radio Systems (SCISRS) Research Program

Introduction
Riverside Research, a not-for-profit organization chartered to advance scientific
research for the benefit of the U.S. government and in the public interest, is pleased to
submit this Capabilities Statement that reviews our background, expertise, and
experience to support the IARPA Securing Compartmented Information with Smart
Radio Systems (SCISRS) Research Program.

Riverside Research’s open innovation R&D model encourages internal and external
collaboration to accelerate innovation, advance science, and expand market opportuni-
ties. It fosters creativity and synergy to encourage and drive innovative solutions to
current and anticipated challenges while allowing us to more easily embrace emerging
technologies. Our Open Innovation Center (OIC) operates a series of geographically-
dispersed laboratories enabling company-funded research that complements our
customer-focused services and provides reach back for our customers.

Particularly relevant to the SCISRS program is the work conducted by our Artificial
Intelligence (AI) and Machine Learning (ML) Laboratory, Optics and Photonics
Laboratory, and Trusted and Resilient Systems Laboratory. These laboratories support
a diverse set of DoD and Intelligence Community customers, including the Defense
Research Projects Agency (DARPA), National Air and Space Intelligence Center
(NASIC), Air Force Research Laboratory (AFRL), U.S. Army Combat Capabilities
Development Command (CCDC) Armaments Center, and National Reconnaissance
Organization (NRO), working closely with numerous industry and academic partners.


State-of-the-Art Equipment and Computing Systems Machine Learning. Current hardware setup includes:

• NVidia DGX-1
  • 8x V100 GPUs
  • 20 core Intel Xeon E5-2698 @ 2.2 GHz
• 2x Lambda Workstations
  • 4x RTX 2080 GPUs per
  • 10 core Intel Core i9 @ 3.7 GHz per
• 1x Lambda Workstation
  • 4x Titan V GPUs
  • 10 core Intel Core i9 @ 3.7 GHz
• 30 TB of high bandwidth network attached storage (NAS)
• Additional Hardware
  • Acquiring Intel Loihi chip and Brainchip Akida processor
  • Multiple COTS edge devices, FPGAs, and small board computers (i.e.: Jetson Nano, Raspberry PI)

 

[Esq.111]

https://www.iarpa.gov/images/PropsersDayPDFs/SCISRS/Riverside_Research_Capabilites.pdf

Riverside Research Capabilities to Support the IARPA Securing Compartmented Information with Smart Radio Systems (SCISRS) Research Program

Introduction
Riverside Research, a not-for-profit organization chartered to advance scientific
research for the benefit of the U.S. government and in the public interest, is pleased to
submit this Capabilities Statement that reviews our background, expertise, and
experience to support the IARPA Securing Compartmented Information with Smart
Radio Systems (SCISRS) Research Program.

Riverside Research’s open innovation R&D model encourages internal and external
collaboration to accelerate innovation, advance science, and expand market opportuni-
ties. It fosters creativity and synergy to encourage and drive innovative solutions to
current and anticipated challenges while allowing us to more easily embrace emerging
technologies. Our Open Innovation Center (OIC) operates a series of geographically-
dispersed laboratories enabling company-funded research that complements our
customer-focused services and provides reach back for our customers.

Particularly relevant to the SCISRS program is the work conducted by our Artificial
Intelligence (AI) and Machine Learning (ML) Laboratory, Optics and Photonics
Laboratory, and Trusted and Resilient Systems Laboratory. These laboratories support
a diverse set of DoD and Intelligence Community customers, including the Defense
Research Projects Agency (DARPA), National Air and Space Intelligence Center
(NASIC), Air Force Research Laboratory (AFRL), U.S. Army Combat Capabilities
Development Command (CCDC) Armaments Center, and National Reconnaissance
Organization (NRO), working closely with numerous industry and academic partners.


State-of-the-Art Equipment and Computing Systems Machine Learning. Current hardware setup includes:

• NVidia DGX-1
  • 8x V100 GPUs
  • 20 core Intel Xeon E5-2698 @ 2.2 GHz
• 2x Lambda Workstations
  • 4x RTX 2080 GPUs per
  • 10 core Intel Core i9 @ 3.7 GHz per
• 1x Lambda Workstation
  • 4x Titan V GPUs
  • 10 core Intel Core i9 @ 3.7 GHz
• 30 TB of high bandwidth network attached storage (NAS)
• Additional Hardware
  • Acquiring Intel Loihi chip and Brainchip Akida processor
  • Multiple COTS edge devices, FPGAs, and small board computers (i.e.: Jetson Nano, Raspberry PI)

View attachment 2707

Afternoon Uiux,

Great find.

That is a juicy one., Very nice.

Regards,
Esq.

 

[Slade]

Nice one Uiux. The video below is four years old but shows the kind of stuff that RiversideResearch work on. Looks right up BrainChip’s alley. It is a great sign that they have sought out Akida.

 

[uiux]

 

[Slade]

Riverside Research virtual tour. Is this your house Uiux?

Open Innovation Center Virtual Tour

Virtual tour of the Riverside Research Open Innovation Center in Dayton, Ohio

www.riversideresearch.org

 

[Diogenese]

https://www.iarpa.gov/images/PropsersDayPDFs/SCISRS/Riverside_Research_Capabilites.pdf

Riverside Research Capabilities to Support the IARPA Securing Compartmented Information with Smart Radio Systems (SCISRS) Research Program

Introduction
Riverside Research, a not-for-profit organization chartered to advance scientific
research for the benefit of the U.S. government and in the public interest, is pleased to
submit this Capabilities Statement that reviews our background, expertise, and
experience to support the IARPA Securing Compartmented Information with Smart
Radio Systems (SCISRS) Research Program.

Riverside Research’s open innovation R&D model encourages internal and external
collaboration to accelerate innovation, advance science, and expand market opportuni-
ties. It fosters creativity and synergy to encourage and drive innovative solutions to
current and anticipated challenges while allowing us to more easily embrace emerging
technologies. Our Open Innovation Center (OIC) operates a series of geographically-
dispersed laboratories enabling company-funded research that complements our
customer-focused services and provides reach back for our customers.

Particularly relevant to the SCISRS program is the work conducted by our Artificial
Intelligence (AI) and Machine Learning (ML) Laboratory, Optics and Photonics
Laboratory, and Trusted and Resilient Systems Laboratory. These laboratories support
a diverse set of DoD and Intelligence Community customers, including the Defense
Research Projects Agency (DARPA), National Air and Space Intelligence Center
(NASIC), Air Force Research Laboratory (AFRL), U.S. Army Combat Capabilities
Development Command (CCDC) Armaments Center, and National Reconnaissance
Organization (NRO), working closely with numerous industry and academic partners.


State-of-the-Art Equipment and Computing Systems Machine Learning. Current hardware setup includes:

• NVidia DGX-1
  • 8x V100 GPUs
  • 20 core Intel Xeon E5-2698 @ 2.2 GHz
• 2x Lambda Workstations
  • 4x RTX 2080 GPUs per
  • 10 core Intel Core i9 @ 3.7 GHz per
• 1x Lambda Workstation
  • 4x Titan V GPUs
  • 10 core Intel Core i9 @ 3.7 GHz
• 30 TB of high bandwidth network attached storage (NAS)
• Additional Hardware
  • Acquiring Intel Loihi chip and Brainchip Akida processor
  • Multiple COTS edge devices, FPGAs, and small board computers (i.e.: Jetson Nano, Raspberry PI)

View attachment 2707

• NVidia DGX-1
  • 8x V100 GPUs
  • 20 core Intel Xeon E5-2698 @ 2.2 GHz
• 2x Lambda Workstations
  • 4x RTX 2080 GPUs per
  • 10 core Intel Core i9 @ 3.7 GHz per
• 1x Lambda Workstation
  • 4x Titan V GPUs
  • 10 core Intel Core i9 @ 3.7 GHz

With all that processing power, what could possibly have prompted them to acquire Akida?

 

[uiux]

Riverside Research virtual tour. Is this your house Uiux?

Open Innovation Center Virtual Tour

Virtual tour of the Riverside Research Open Innovation Center in Dayton, Ohio

www.riversideresearch.org

Too much colour Slade

Open Innovation Center Virtual Tour

Virtual tour of the Riverside Research Open Innovation Center in Dayton, Ohio

www.riversideresearch.org

@Diogenese they've had TrueNorth for a while it seems - looks like they want something decent now

 

[Foxdog]

Yes, but when do we start hearing from all of these researchers/users about what they actually think of AKIDA and it's capabilities and applications? What will it take for AKIDA to become a household name?

 

[uiux]

Yes, but when do we start hearing from all of these researchers/users about what they actually think of AKIDA and it's capabilities and applications? What will it take for AKIDA to become a household name?

Wouldn't be surprised to never hear about this research ever again



Not because I think it won't go anywhere but because this slide deck is unclassified and the next ones will most likely be very classified

 

[Fact Finder]

Wouldn't be surprised to never hear about this research ever again



Not because I think it won't go anywhere but because this slide deck is unclassified and the next ones will most likely be very classified

Hi uiux
I think this is right.

Where we will start to see the positive commentary will be from researchers in universities who are not tied to defence who Sean Hehir is inviting to use AKIDA. Working on the basis that it takes about six months to develop and market white goods I would think just like income is expected in second half 2022 that these research papers will start to appear around then as well.

Over time I have read lots of research and no doubt you have where the university researcher arrives at the point where the next stage is to test what they are doing on an actual neuromorphic chip. They have the groundwork all done and ready to go so a six month turn around for these researchers seems entirely feasible even taking account of peer reviews as they already have the peer reviewer from the first paper up to speed.

We are moving into a very exciting part of this journey and I cannot wait to see what these researchers achieve and have to say once the full enormity of what AKIDA brings is revealed to them.

My opinion only DYOR
FF

AKIDA BALLISTA

 

[Foxdog]

Thanks for prompt reply uiux.

So is it fair to say that with such revolutionary technology and the high stakes involved, not to mention that some customers exist in a highly classified environment, that our only true indication of BRN's success and growth will be through the compulsory declaration of revenue? Or do you expect that Mercedes and similar end users might use AKIDA as a point of difference for sales?

Are we ever going to see public validation of the tech where everyone knows that if you don't have AKIDA then you're not really in the game?

 

[uiux]

Thanks for prompt reply uiux.

So is it fair to say that with such revolutionary technology and the high stakes involved, not to mention that some customers exist in a highly classified environment, that our only true indication of BRN's success and growth will be through the compulsory declaration of revenue? Or do you expect that Mercedes and similar end users might use AKIDA as a point of difference for sales?

Are we ever going to see public validation of the tech where everyone knows that if you don't have AKIDA then you're not really in the game?

not really sure Foxdog


From what I have read over the years and certainly within the last few days, all of the military/defense/acronym organisations seem to only like True North, BrainChip Akida and Intel Loihi.

 

[Foxdog]

• NVidia DGX-1
  • 8x V100 GPUs
  • 20 core Intel Xeon E5-2698 @ 2.2 GHz
• 2x Lambda Workstations
  • 4x RTX 2080 GPUs per
  • 10 core Intel Core i9 @ 3.7 GHz per
• 1x Lambda Workstation
  • 4x Titan V GPUs
  • 10 core Intel Core i9 @ 3.7 GHz

With all that processing power, what could possibly have prompted them to acquire Akida?

The same thing that prompted them to acquire Intel Loihi chip

 

[Foxdog]

not really sure Foxdog


From what I have read over the years and certainly within the last few days, all of the military/defense/acronym organisations seem to only like True North, BrainChip Akida and Intel Loihi.




View attachment 2716

We'll I hope that AKIDA proves to be significantly better because in my experience Defence procurement is influenced by 'name brands' - much easier to pass the public 'smell test' when you're buying from a commonly known name i.e. Intel, even if it isn't actually the best option. Politics plays a huge role....

 

[Foxdog]

It's the opposite for commercial companies like Merc as a left-field innovation from a no name minnow is marketing gold......

 

[uiux]

It will come down to the strengths of Akida and its unique capabilities, described as "game changing" by many people so far


The fact that it is ready now is also a massive boost

 

[Diogenese]

The same thing that prompted them to acquire Intel Loihi chip

Hi Foxdog,

I was trying to draw an inference from the DOE/NASA SBIRs that @uiux posted, which followed the announcement that NASA had signed up to the Akida Early Adopters Programme.

I'd like to think that some unsubtle hints are being dropped.

 

[Foxdog]

Perhaps, but really what we are seeing so far is testing or pilot schemes to assess if AKIDA is worth integrating, or perhaps worthy of new technology development.

It's a catch 22 though - the more significant a technological shift that AKIDA represents, the less likely we are to hear about it because each implementer will want to keep their advantage hidden.

Back to disclosure of revenue......'where did all that money come from?'

 

[Dhm]

Should Riverside Research be placed on @greenwaves excellent Speculative Investigation Wall?

 

[Fact Finder]

Perhaps, but really what we are seeing so far is testing or pilot schemes to assess if AKIDA is worth integrating, or perhaps worthy of new technology development.

It's a catch 22 though - the more significant a technological shift that AKIDA represents, the less likely we are to hear about it because each implementer will want to keep their advantage hidden.

Back to disclosure of revenue......'where did all that money come from?'

The full wording of the following will provide solid evidence of the FACT that income may be the only reward when dealing with Defence applications. The question of whether Brainchip engaged with this SBIR is live however it can be seen how sensitive SNN is dealt with:
Implementing Neural Network Algorithms on Neuromorphic Processors

Navy SBIR 20.2 - Topic N202-099

Naval Air Systems Command (NAVAIR) - Ms. Donna Attick navairsbir@navy.mil

Opens: June 3, 2020 - Closes: July 2, 2020 (12:00 pm ET)


N202-099 TITLE: Implementing Neural Network Algorithms on Neuromorphic Processors


RT&L FOCUS AREA(S): Artificial Intelligence/ Machine Learning, General Warfighting Requirements (GWR)

TECHNOLOGY AREA(S): Air Platform

OBJECTIVE: Deploy Deep Neural Network algorithms on near-commercially available Neuromorphic or equivalent Spiking Neural Network processing hardware.

DESCRIPTION: Biological inspired Neural Networks provide the basis for modern signal processing and classification algorithms. Implementation of these algorithms on conventional computing hardware requires significant compromises in efficiency and latency due to fundamental design differences. A new class of hardware is emerging that more closely resembles the biological Neuron/Synapse model found in Nature and may solve some of these limitations and bottlenecks. Recent work has demonstrated significant performance gains using these new hardware architectures and have shown equivalence to converge on a solution with the same accuracy [Ref 1].

The most promising of the new class are based on Spiking Neural Networks (SNN) and analog Processing in Memory (PiM), where information is spatially and temporally encoded onto the network. A simple spiking network can reproduce the complex behavior found in the Neural Cortex with significant reduction in complexity and power requirements [Ref 2]. Fundamentally, there should be no difference between algorithms based on Neural Network and current processing hardware. In fact, the algorithms can easily be transferred between hardware architectures [Ref 4]. The performance gains, application of neural networks and the relative ease of transitioning current algorithms over to the new hardware motivates the consideration of this topic.

Hardware based on Spiking Neural Networks (SNN) are currently under development at various stages of maturity. Two prominent examples are the IBM True North and the INTEL Loihi Chips, respectively. The IBM approach uses conventional CMOS technology and the INTEL approach uses a less mature memrisistor architecture. Estimated efficiency performance increase is greater than 3 orders of magnitude better than state of the art Graphic Processing Unit (GPUs) or Field-programmable gate array (FPGAs). More advanced architectures based on an all-optical or photonic based SNN show even more promise. Nano-Photonic based systems are estimated to achieve 6 orders of magnitude increase in efficiency and computational density; approaching the performance of a Human Neural Cortex. The primary goal of this effort is to deploy Deep Neural Network algorithms on near-commercially available Neuromorphic or equivalent Spiking Neural Network processing hardware. Benchmark the performance gains and validate the suitability to warfighter application.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract.

PHASE I: Develop an approach for deploying Neural Network algorithms and identify suitable hardware, learning algorithm framework and benchmark testing and validation methodology plan. Demonstrate performance enhancements and integration of technology as described in the description above. The Phase I effort will include plans to be developed under Phase II.

PHASE II: Transfer government furnished algorithms and training data running on a desktop computing environment to the new hardware environment. An example algorithm development frame for this work would be TensorFlow. Some modification of the framework and/or algorithms may be required to facilitate transfer. Some optimization will be required and is expected to maximize the performance of the algorithms on the new hardware. This optimization should focus on throughput, latency, and power draw/dissipation. Benchmark testing should be conducted against these metrics. Develop a transition plan for Phase III.

It is probable that the work under this effort will be classified under Phase II (see Description section for details).

PHASE III DUAL USE APPLICATIONS: Optimize algorithm and conduct benchmark testing. Adjust algorithms as needed and transition to final hardware environment. Successful technology development could benefit industries that conduct data mining and high-end processing, computer modeling and machine learning such as manufacturing, automotive, and aerospace industries.

(20.2 SBIR) Navy - Implementing Neural Network Algorithms on Neuromorphic Processors

www.navysbir.com