heres
another example of successful "dot joining" aka
industry research.
I have posted this SBIR a few times but more recently included it in some analysis of BrainChips recently granted patents:
https://hotcopper.com.au/posts/59005473/singleyou can read the reference at the bottom to the navy SBIR
https://www.navysbir.com/n20_2/N202-099.htm
Implementing Neural Network Algorithms on Neuromorphic Processors
OBJECTIVE: Deploy Deep Neural Network algorithms on near-commercially available Neuromorphic or equivalent Spiking Neural Network processing hardware.
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.
The SBIR specifically references the research:
4. Esser, S., Merolla, P., Arthur, J., Cassidy, A., Appuswamy, R., Andreopoulos, A., . . . Modha, D. “Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing.” IBM Research: Almaden, May 24, 2016. https://arxiv.org/pdf/1603.08270.pdf
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now you can see that this SBIR has progressed to phase 2 and BrainChip has been referenced directly:
https://xcelaero.com/blog/press-release-2/3-17-2022-press-release-bhtech-wins-phase-ii-sbir-award-for-implementing-neural-network-algorithms-on-neuromorphic-processors/BHTech wins Phase II SBIR Award for Implementing Neural Network Algorithms on Neuromorphic ProcessorsThe generation of real-time insights for the warfighter is an increasingly important area of interest, especially due to the growth of Electronic Warfare challenges. These insights require faster processors and smarter models that can be deployed at the edge in low Size, Weight and Power (SWaP) configurations. Traditional von-Neumann based computing architectures are challenged by the complex learning models, low power budget and real-time needs of the warfighter. To mitigate this limitation, BHTech has proposed to the United States Navy an implementation strategy using neuromorphic processors to accommodate modern SWaP and performance requirements of the warfighter.
Bascom Hunter Technologies has recently been awarded a Phase 2 Navy SBIR award for BHTech’s proposal on implementing neural network algorithms on neuromorphic processors.During phase 1, BHTech has demonstrated in the superior performance of photonic based neurons within Continuous Neuromorphic Computing architectures for both electronic and hybrid-photonic hardware. In Phase 2, we will extend that work to create designs for a Neuromorphic Toolbox of solutions providing Electronic, Spiking Electronic and Hybrid Photonic hardware for Neural Network topologies. The Phase 2 Option will develop these designs into benchtop prototypes. The Phase 2 Option will also include the development of a VPX Neuromorphic Hardware that is HOST compatible. In Phase 3 we will be looking at optimizing solutions for the Navy and creating deployable Neuromorphic Hardware. This will be based on the Neuromorphic Hardware Toolbox that was started in Phase 2 as well as the development of BASE (Bascom Hunter’s AI Software Environment), which will aid the rapid migration of machine learning algorithms from Desktop Computing Systems to Edge Computing modules.Neuromorphic Processors provide a realistic solution to obtaining real-time insights for the warfighter by leveraging an architecture that more closely resembles the Human Brain and are better suited to run Neural Network models. Our toolbox approach allows the best hardware architectures to be matched with the best software solutions, enabling the rapid conversion of cutting-edge technology into ruggedized, modular hardware. Our Hardware and Software Toolboxes will bring immediate benefits to warfighters in the Navy and beyond by extracting actionable insights in real-time at the edge (eliminating the latency problem when processing in the cloud). One application is the real-time identification of Radio Frequency (RF) emitters using Neuromorphic processors operating via trained Neural Networks.