Here are twos examples where previous 'dot joining', aka industry research, has turned out to actually involve Akida.
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I originally discovered and posted a NASA SBIR proposal for neuromorphic enhanced cognitive radio on 31/03/21
https://hotcopper.com.au/posts/52187947/single
Phase 1:
Deep Neural Net and Neuromorphic Processors for In-Space Autonomy and Cognition
NASA is seeking innovative neuromorphic processing methods and tools to enable autonomous space operations on platforms constrained by size, weight, and power (SWaP). To address this need, Intellisense Systems, Inc. (Intellisense) proposes to develop a new Neuromorphic Enhanced Cognitive Radio (NECR) device based on neuromorphic processing and its efficient implementation on neuromorphic computing hardware. NECR is a low-SWaP cognitive radio that integrates the open source software radio framework with a new neuromorphic processing module to automatically process the incoming radio signal, identify the modulation types and parameters of the signal, and send the identification results to the controller module to properly decode the incoming signal. Due to its efficient implementation on neuromorphic computing hardware, NECR can be easily integrated into SWaP-constrained platforms in spacecraft and robotics to support NASA missions in unknown and uncharacterized space environments, including the Moon and Mars. In Phase I, we will develop the concept of operations (CONOPS) and key algorithms, integrate a Phase I prototype software in a simulated environment to demonstrate its feasibility, and develop aPhase II plan with a path forward.
In Phase II, the NECR algorithms will be further matured, implemented on commercial off-the-shelf neuromorphic computing hardware, and then integrated with radio frequency (RF) modules and radiation-hardened packaging into a Phase II working prototype device. The Phase II prototype will be tested to demonstrate its fault and mission tolerances and delivered with documentation and tools to NASA for applications to CubeSat, SmallSat, and rover flight demonstrations.
Potential NASA Applications (Limit 1500 characters, approximately 150 words):NECR technology will have many NASA applications due to its low-SWaP and low-cost cognitive sensing capability. It can be used to enhance the robustness and reliability of space communication and networking, especially cognitive radio devices. NECR can be directly transitioned to the Human Exploration and Operations Mission Directorate (HEOMD) Space Communications and Navigation (SCaN) Program to address the needs of the Cognitive Communications project.
Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words):NECR technology’s low-SWaP and low-cost cognitive sensing capability will have many non-NASA applications. The NECR technology can be integrated into commercial communication systems to enhance cognitive sensing and communication capability. Automakers can also integrate the NECR technology into automobiles for cognitive sensing and communication.
Phase 2:Estimated Technology Readiness Level (TRL) :Begin: 3End: 4Technical Abstract (Limit 2000 characters, approximately 200 words):Intellisense Systems, Inc. proposes in Phase II to advance development of a Neuromorphic Enhanced Cognitive Radio (NECR) device to enable autonomous space operations on platforms constrained by size, weight, and power (SWaP). NECR is a low-size, -weight, and -power (-SWaP) cognitive radio built on the open-source framework, i.e., GNU Radio and RFNoC™, with new enhancements in environment learning and improvements in transmission quality and data processing. Due to the high efficiency of spiking neural networks and their low-latency, energy-efficient implementation on neuromorphic computing hardware, NECR can be integrated into SWaP-constrained platforms in spacecraft and robotics, to provide reliable communication in unknown and uncharacterized space environments such as the Moon and Mars. In Phase II, Intellisense will improve the NECR system for cognitive communication capabilities accelerated by neuromorphic hardware. We will refine the overall NECR system architecture to achieve cognitive communication capabilities accelerated by neuromorphic hardware, on which a special focus will be the mapping, optimization, and implementation of smart sensing algorithms on the neuromorphic hardware. The Phase II smart sensing algorithm library will include Kalman filter, Carrier Frequency Offset estimation, symbol rate estimation, energy detection- and matched filter-based spectrum sensing, signal-to-noise ratio estimation, and automatic modulation identification. These algorithms will be implemented on COTS neuromorphic computing hardware such as Akida processor from BrainChip, and then integrated with radio frequency modules and radiation-hardened packaging into a Phase II prototype. At the end of Phase II, the prototype will be delivered to NASA for testing and evaluation, along with a plan describing a path to meeting fault and tolerance requirements for mission deployment and API documents for integration with CubeSat, SmallSat, and rover for flight demonstration.Potential NASA Applications (Limit 1500 characters, approximately 150 words):NECR technology will have many NASA applications due to its low-SWaP and low-cost cognitive sensing capability. It can be used to enhance the robustness and reliability of space communication and networking, especially cognitive radio devices. NECR can be directly transitioned to the Human Exploration and Operations Mission Directorate (HEOMD) Space Communications and Navigation (SCaN) Program, CubeSat, SmallSat, and rover to address the needs of the Cognitive Communications project.Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words):NECR technology’s low-SWaP and low-cost cognitive sensing capability will have many non-NASA applications. The NECR technology can be integrated into commercial communication systems to enhance cognitive sensing and communication capability. Automakers can integrate the NECR technology into automobiles for cognitive sensing and communication.
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another example is a Quantum Ventura STTR which I originally posted on 31/08/21 but later reposted:
https://hotcopper.com.au/posts/55706956/single
Phase 1:
Department of Energy: "Cyber threat-detection using neuromorphic computing"
https://govtribe.com/award/federal-grant-award/project-grant-desc0021562
Awarded Vendor
Quantum Ventura, Inc. - 7K3W2
Project Grant DESC0021562. Funded by the Office of Science (DOE). Awarded to Quantum Ventura, Inc.. Awarded on Feb 22, 2021. CFDA 81.049 - Office of Science Financial Assistance Program
Our Summary
REALTIME NEUROMORPHIC CYBER-AGENTS (CYBER-NEURORT)
https://science.osti.gov/-/media/sb...e-I-Release-1Award-Listing01282021.xlsx?la=en
Quantum Ventura, Inc.
$ 250,000
Realtime Neuromorphic Cyber-Agents (Cyber-NeuroRT)
Cybersecurity in HPC environments operates at much larger scales than traditional IT domains and the traditional Machine learning networks are not fast enough to handle large volumes of computations. Neural networks combined with Edge-based hardware-resident next-generation technologies such as neuromorphic processors can monitor and even predict events in high throughput environments and hence provide an up-and-coming solution to cybersecurity in HPC. To that end, we propose to develop a real-time HPC-scale neuromorphic cyber agent called Cyber-NeuroRT. Cyber-NeuroRT will be a real-time neuromorphic processor based monitoring tool to predict and alert cybersecurity threats and warnings using an ensemble of unsupervised and semi-supervised Machine Learning algorithms. Cyber- NeuroRT is a combination of software cum hardware appliance with neuromorphic processor chips and this will be installed at a server level or at distributed node-level for cyber threat detection. It uses Spiking Neural Networks (SNNs) to learn new attack vectors in addition to labeling known attacks and uses an ensemble of semi-supervised and unsupervised algorithms. Cyber-NeuroRT is a combination of hardware cum software appliance with neuromorphic processor chips that can be installed at a system level or at distributed node-level for cyber threat detection. Neuromorphic based processors excel in identifying patterns and intrusion detection with over 100x efficiency as compared to a GPU based system. In addition, neuromorphic systems can learn to adapt to novel attack vectors. We will use different training techniques like CNN to SNN conversion, direct backpropagation training through surrogate gradient methods or local unsupervised Spike Timing Dependent Plasticity (STDP) enabled approaches. Neuromorphic hardware appliance will have the ability to connect up to 64 neuromorphic processors if additional processing power is required. The neuromorphic processor-based system can surpass the traditional intrusion detection tools (IDS). Some of the features of Cyber-NeuroRT shall include: (a) Ability to monitor, predict and provide system wide alerts of impending cybersecurity threats and warnings by collecting and prioritizing data from real time logging tools/ analysis tools including Zeek (Bro) Logs, PerfSonar, ftp logs, user behavior data, or any type of relevant logs, and other types of sensor data including IoT devices, HVAC, power systems, etc. Initially, we will work on Zeek (Bro) log files in Phase 1 and 2; (b) Ability to process the data system-wide at an unprecedented scale enabling adaptive, streaming analysis for monitoring and maintaining large-scale scientific computing integrity; (c) Training SNNs through direct backpropagation training is computationally expensive due to the gradient descent updates through time. So, potentially we could train our models using ORNL’s Summit type super computers and then perform actual detection of threats using neuromorphic processors; and (d) In addition to neuromorphic processors at the server level, we will also provide an option to process larger Machine Learning Models that can be hosted on next-generation neuromorphic systems under development.
Phase 2:
https://science.osti.gov/-/media/sbir/excel/2022/FY22-_Phase-II_Release-1_Award.xlsx
Department of Energy: "Cyber threat-detection using neuromorphic computing"
Realtime Neuromorphic Cyber-Agents (Cyber-NeuroRT)
Based on Phase 1 Feasibility and proof-of-concept developed for Cyber-NeuroRT, we propose to develop a full-fledged prototype. Cyber-NeuroRT, a real-time neuromorphic processor-based monitoring tool to predict and alert cyber threats and warnings using the Neuromorphic Platforms of Intel Loihi and Akida and develop a user-friendly dashboard for analysts. We will expand our capability to detect complex cyber-attacks in near real-time and develop new techniques to detect unknown and unfamiliar cyberattacks using novel neuromorphic unsupervised learning techniques.
STTR $ 1,650,000.00
(20min delay)