In November, 2022 this paper authored by researchers at Quantum Ventura and Penn State University was published and posted by myself here and elsewhere:

Cyber-Neuro RT: Real-time Neuromorphic Cybersecurity

Author links open overlay panelWyler Zahm a, Tyler Stern a, Malyaban Bal b, Abhronil Sengupta b, Aswin Jose a, Suhas Chelian a, Srini Vasan a

a

Quantum Ventura, 1 South Market Street, Suite 1715, San Jose, CA 95113, USA

b

Penn State University, University Park, PA, 16802 USA

Available online 26 November 2022, Version of Record 26 November 2022

5. DISCUSSION AND CONCLUSION

The feasibility of a real-time HPC-scale neuromorphic cybersecurity system was assessed.

We call the system Cyber-Neuro RT.

We used algorithms including full precision deep learning (DL), deep learning to spiking neural network (DL-to-SNN) conversions, and design exploration within SNNs. Neuromorphic implementations of the full precision network were roughly comparable to the full precision model in terms of accuracy but would offer significant savings in power and cost. Thus the approach has been validated. We will further explore the concept along several dimensions such as further tuning networks to reduce false positives, trying the DL-to-SNN converted algorithms on hardware, and using larger or different datasets. In addition, Cyber-Neuro RT can also be deployed on GPUs if a site prefers to use GPUs over neuromorphic computers (e.g., the site may be performing other tasks on the GPUs, etc.)

For DL-to-SNN conversion, we determined that conversion of ANNs to SNNs varies across different neuromorphic vendors. Intel offers a cloud-accessible neuromorphic processing infrastructure and an API for interacting with third-party tools (e.g., NengoDL and SNN Toolbox) whereas BrainChip provides a stand-alone proprietary development/simulation environment (MetaTF SDK).

These conversion tools provide a theoretically similar process for transformation from ANN to SNN, but with differences in the ANN design and requirements, as well as different conversion results.

In addition, Intel and BrainChip will provide new or updated toolboxes (e.g., SLAYER for Intel), new algorithms, etc. which are likely to affect performance. Akida released their products in late 2021.

Intel, on the other hand, is only offering their chips for research purposes at this time.

Due to this constraint, our experiments were conducted using Intel’s cloud environment and the stand-alone simulation environment for Akida.

There are drastic differences between software simulation and hardware and we intend to study those further.

For design space exploration, there are design and control time knobs which can reduce inference latency while offering the same or slightly less accuracy compared to full precision models.

Still other explorations are possible such as backpropagation through time or semi-supervised or unsupervised learning with spike timing dependent plasticity (STDP) learning rules.

Acknowledgements:

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research program under Award Number DE-SC0021562.

https://www.sciencedirect.com/science/article/pii/S1877050922017938/pdf?md5=d9c849d37eb0815269240514a9efbe22&pid=1-s2.0-S1877050922017938-main.pdf

And then in 2023 the following was announced:

BrainChip and Quantum Ventura Partner to Develop Cyber Threat Detection

Laguna Hills, Calif. – May 15, 2023 – BrainChip Holdings Ltd(ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, announced today Quantum Ventura Inc., a San Jose-based provider of AI/ML research and technologies, will use BrainChip’s Akida™ technology to develop new cyber threat-detection tools.

In this federally funded phase 2 program, Quantum Ventura is creating state-of-the-art cybersecurity applications for the U.S. Department of Energy under the Small Business Innovation Research (SBIR) Program. The program is focused on “Cyber threat-detection using neuromorphic computing,” which aims to develop an advanced approach to detect and prevent cyberattacks on computer networks and critical infrastructure using brain-inspired artificial intelligence.

“Neuromorphic computing is an ideal technology for threat detection because of its small size and power, accuracy, and in particular, its ability to learn and adapt, since attackers are constantly changing their tactics,” said Srini Vasan, President and CEO of Quantum Ventura Inc. “We believe that our solution incorporating BrainChip’s Akida will be a breakthrough for defending against cyber threats and address additional applications as well.”

“This project with the Department of Energy offers an ideal opportunity to demonstrate how Akida opens up new possibilities in cybersecurity, including the ability to run complex AI algorithms at the edge, reducing the dependency on the cloud” said Rob Telson, Vice President of Ecosystems & Partnerships at BrainChip. “We are excited about the progress that Quantum Ventura are making with BrainChip in this project which is extremely vital to the safety of the nation’s infrastructure.”

The Akida neural processor and AI IP can find unknown, repeating patterns in vast amounts of noisy data, which is an asset in cyber threat detection. Once Akida learns what normal network traffic patterns look like, it can detect malware, attack signatures, and other types of malicious activity. Because of Akida’s unique ability to learn on device in a secure fashion, without need for cloud retraining, it can quickly learn new attack patterns, enabling it to easily adapt to emerging threats.

BrainChip IP supports incremental learning, on-chip learning, and high-speed inference with unsurpassed performance in micro watt to milli-watt power budgets, ideal for advanced AI/ML devices such as intelligent sensors, medical devices, high-end video-object detection, and ADAS/autonomous systems. Akida is an event-based technology that is inherently lower power than conventional neural network accelerators, providing energy efficiency with high performance for partners to deliver AI solutions previously not possible on even battery-operated or fan-less embedded, edge devices.

About Quantum Ventura Inc.

Headquartered in the heart of Silicon Valley in historic San Jose downtown, Quantum Ventura Inc. is in the business of creating innovative and groundbreaking systems and technologies in the areas of Artificial Intelligence/Machine Learning, AI/ML Verification and Validation, Cybersecurity, Secure Mobile technology (Diamond Droid) and HPC-driven Big Data Analytics. Quantum Ventura’s R&D division, QuantumX Research Labs, undertakes R&D services in providing advanced technology solutions to federal agencies and corporations throughout the U.S. Quantum Ventura excels in developing concepts into market-focused products and customer-driven solutions, designing creative solutions, and building unique products for challenging problems with complete end-to-end solutions, components, and unsurpassed technical expertise. For more information visit https://www.quantumventura.com/.

And then in 2024 the following was announced:

BrainChip Adds Penn State to Roster of University AI Accelerators

Laguna Hills, Calif. – May 8, 2024 – BrainChip Holdings Ltd(ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, today announced that Pennsylvania State University has joined the BrainChip University AI Accelerator Program, ensuring students have the tools and resources necessary to help develop leading-edge intelligent AI solutions.

Penn State is a top-ranked research university founded with a mission of high-quality teaching, expert research, and global service. The Neuromorphic Computing Lab located in Penn State’s School of Electrical Engineering and Computer Science aims to create a new type of computer that can learn and operate with brain-scale efficiency. In joining the BrainChip University AI Accelerator Program, EECS students will now have access to cutting-edge neuromorphic technology that will directly affect their communities and solve big problems that may positively impact humanity.

BrainChip’s University AI Accelerator Program provides platforms and guidance to students at higher education institutions with AI engineering programs. Students participating in the program have access to real-world, event-based technologies offering unparalleled performance and efficiency to advance their learning through graduation and beyond.

“As part of Penn State’s Neuromorphic Computing Lab, we are dedicated to bridging the gap between nanoelectronics, neuroscience and machine learning,” said Abhronil Sengupta, EECS Assistant Professor. “By joining BrainChip’s University AI Accelerator Program, we are better positioned to provide our students with resources needed to enable further research and study into neuromorphic computing. By leveraging BrainChip’s technology with our inter-disciplinary approach to data science and AI, we ensure students are ready to develop solutions for the world’s most pressing issues.”

Some may recall I posted an interview with Mike Davies from early this year where he said that Intel hoped to build commercial traction for Loihi technology by engaging with NASA and Space technology companies. Some may recall I pointed out the bl..ding obvious that Brainchip was also following this course and was clearly being successful when regard is had to the EDGX-1 Brain, ANT61 Space Service Robot, ESA, Airbus, Gaisler etc announcements not to mention NASA, BRE, Quantum Ventura, ISL etc; "

Recently I was sent the following article by an American investor in Brainchip regarding the founders of Palantir and one thing stood out to me was that they had experienced exactly the same barriers which have been presented to Brainchip and that their road to succcess was initially paved via companies in the Defence and Homeland Security Space.

https://www.nytimes.com/2024/08/17/style/alex-karp-palantir.html?smid=nytcore-ios-share&referringSource=articleShare&sgrp=c-cb

For those who might have forgotten the first time we heard about Quantum Ventura was when it published a paper as a result of a project funded by the US Department of Homeland Security to explore the feasibility of developing a handheld device to detect contraband coming through ports. In this paper Quantum Ventura tested a $US30,000 Nvidia GPU against AKD1000 and found that AKD1000 at a cost of about $US50.00 could be used.

My opinion only DYOR

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