The following is all publicly available information. This information leads me to conclude that Lockheed Martin is working with Brainchip and that Quantum Ventura and Lockheed Martin will work together to commercialise the AKIDA cybersecurity solution and that Brainchip may also be involved in a number of additional projects involving Lockheed Martin and Quantum Ventura:

My opinion only DYOR

Fact Finder

FACT ONE

The use of AKIDA Neuromorphic Computing for cyber-security systems is not a novel idea as it has been successfully explored by a range of other researchers independent of Quantum Ventura and Brainchip. For example:

Adaptive Cyber Defense: Leveraging Neuromorphic Computing for Advanced Threat Detection and Response

A Srivastava, V Parmar, S Patel… - … Computing and Smart …, 2023 - ieeexplore.ieee.org

… The Intel Loihi 1 and BrainChip AkidaNeuromorphic Platforms are utilised by this tool in order to make forecasts and deliver alerts regarding cybersecurity concerns and warnings. The …

Adaptive CyberDefense: Leveraging Neuromorphic Computing for Advanced Threat Detection and Response

Aviral Srivastava, Viral Parmar, Samir Patel, Akshat Chaturvedi

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), 1557-1562, 2023

As the complexity of the digital landscape evolves, so does the sophistication of cyber threats, necessitating advanced cybersecurity measures. Despite significant strides in threat detection and response using machine learning and deep learning techniques, these systems grapple with high false positive rates, limited adaptability to evolving threats, and computational inefficiency in real-time data processing. This study proposes to delve into the potential of Neuromorphic Computing (NC) to address these challenges. Inspired by the human brain’s principles, NC offers rapid, efficient information processing through Spiking Neural Networks (SNNs) and other brain-inspired architectures. The study hypothesizes that integrating NC into cyberdefence could enhance threat detection, response times, and adaptability, thereby bolstering cybersecurity systems’ resilience. However, the implementation of NC in cybersecurity is fraught with challenges, including scalability, compatibility with existing infrastructures, and the creation of secure, robust neuromorphic systems. This study elucidates these challenges, proposes potential solutions, and highlights future research directions in this promising field. With focused research and development, NC could revolutionize cybersecurity, enhancing the defence mechanisms of the digital ecosystems against the relentless onslaught of cyberthreats.

The study analyses that the incorporation of NC into cybersecurity is not only feasible but also necessary in increasingly digital world.

View at ieeexplore.ieee.org

FACT TWO

The use of AKIDA technology is specifically provided for in the Quantum Ventura SBIR application which commenced in 2021 and completed on 3 April, 2024.

Realtime Neuromorphic Cyber-Agents (Cyber-NeuroRT)

Amount: $1,650,000 Topic: C51-03a

As part of Phase 1 feasibility study, we evaluated the viability to develop a real-time HPC-scale neuromorphic cyber agent software called Cyber-NeuroRT. We evaluated several scalable neuromorphic techniques to detect and predict cybersecurity threats, compared full precision machine learning models with neuromorphic models and developed an end-to-end Proof of Concept (POC). Upon completion of Phase 2 prototype, we will produce dramatic reductions in latency and power--up to 100x--without sacrificing accuracy. This will enable quicker response times and savings in operating costs. Cyber-NeuroRT will be a real-time neuromorphic processor-based monitoring tool to predict and alert cybersecurity threats and warnings using the Neuromorphic Platforms of Intel Loihi 1 and BrainChip Akida. For our Phase 1 POC development, we used 450,000 Zeek log entries with a mixture of normal and malicious data for training the supervised ML models. As part of our study, we covered the following: Cyber Attack types covered – 8 attack types: backdoor, DDOS, DOS, injection, password, ransomware, scanning and XSS, Source files – Zeek log files and Packet Capture Format files (PCAP) containing both malicious and normal records. We used both Supervised and Unsupervised algorithms. We used algorithms including SNN and CNN-to-SNN conversion with unsupervised learning and supervised learning rules. To build a full-fledged prototype of Cyber-Neuro RT, we plan to transition the proof-of-concept work to scale to a large data set with additional threat types and other datasets from an HPC environment. HPC environments operate at larger scales than traditional IT domains and our solution should be able to monitor and predict events at more than 160,000 inferences per second. Tuning of Spike Neural Networks (SNN) parameters such as precision of weights and number of neurons used are two software parameters to explore. The chip can be tuned between high v. low power modes and performance can be studied as a function of power draw. Evaluation will be performed across a variety of datasets and parameter settings to estimate deployment performance. We will work on efficiency scaling of SNN algorithms in terms of accuracy and hardware metrics like power and energy consumption. Since cybersecurity attack classification is a temporal process, we will leverage recent advancements in the algorithm community to map temporal dynamics of SNNs to recurrent architectures. Further, to adapt to novel attack vectors, we will explore unsupervised learning techniques in a dynamic network architecture where we will grow or shrink the network as and when novel attack vectors arise. We will also perform an algorithm-hardware co-design analysis by ensuring that our algorithm proposals cater to and consider specific constraints from Akida or Loihi processors like network size, bit quantization levels, among others. 3.1 Some of the features of Cyber-NeuroRT prototype shall include: Ability to monitor, predict and provide system wide alerts of impending cybersecurity threats and warnings at scale by collecting and prioritizing data from Zeek logs and PCAP files streamed in real-time or batch. We will expand and refine 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. Compare performance of threat detection between neuromorphic processing vs GPU-based systems and compare between Akida and Intel Loihi processors. Ability to process the data system-wide at an unprecedented scale enabling adaptive, streaming analysis for monitoring and maintaining large-scale scientific computing integrity. Dashboards for security administrators and security analysts

Realtime Neuromorphic Cyber-Agents (Cyber-NeuroRT)

Award Information

Agency:Department of Energy

Branch:N/A

Contract:DE-SC0021562

Agency Tracking Number:0000263950

Amount:$1,650,000.00

Phase:Phase II

Program:STTR

Solicitation Topic Code:C51-03a

Solicitation Number:N/A

Timeline

Solicitation Year:2021

Award Year:2022

Award Start Date (Proposal Award Date):2022-04-04

Award End Date (Contract End Date):2024-04-03

Small Business Information

QUANTUM VENTURA INC

1 S Market Street suite 1715

San Jose, CA 95113

United States

DUNS:080159595

HUBZone Owned:Yes

Woman Owned:No

Socially and Economically Disadvantaged:Yes

Principal Investigator

Name: Srini Vasan
Phone: (424) 227-1417
Email: [email protected]

Business Contact

Name: Srini Vasan
Phone: (424) 227-1417
Email: [email protected]

Research Institution

Name: Pennsylvania State University Harrisburg
Address:

777 W Harrisburg Pike BLDG Olmsted

State College, PA 17057-0000

United States

Type: Nonprofit College or University

FACT THREE

Brainchip and Quantum Ventura announce they have partnered on 15 May, 2023 to work on this SBIR. This date is important as it comes at the precise point when Quantum Ventura need to decide whether they would move forward to a prototype using COTS AKIDA or the Loihi research chip.

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.

FACT FOUR

On 8 May, 20 May, 2024 Brainchip announces Penn State is joining Brainchips university accelerator program.

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.”

BrainChip’s neural processor, Akida™ IP is an event-based technology that is inherently lower power when compared to conventional neural network accelerators. Lower power affords greater scalability and lower operational costs. BrainChip’s Akida supports incremental learning and high-speed inference in a wide variety of use cases. Among the markets that BrainChip’s Essential AI technology will impact are the next generation of smart cars, smart homes of today and tomorrow, and industrial IoT.

“For universities like Penn State that pride themselves on delivering a world-class education, forming partnerships with leaders like BrainChip is an ideal way to give students access to frontier technology,” said Tony Lewis, CTO of BrainChip. “We hope by making Neuromorphic Event Based technology readily available, we can give students hands on experience with a new paradigm in computation and open fundamentally new research directions in engineering. Neuromorphic event-based computing may be a solution to the inefficiencies inherent in conventional AI computation that is of growing concern to the public. It’s important that students engage early and with the right tools. BrainChip is happy to help.”

FACT FIVE

Quantum Ventura partnered with Bodkin Design in the following project where they highlighted the benefits of using AKIDA technology over a Nvidia GPU and coincidentally cite the work they were doing with AKIDA is cyber-security.

https://www.sciencedirect.com/science/article/pii/S1877050922017860

FACT SIX

Quantum Ventura is partnered with Penn State University, Lockheed Martin and Bodkin Design and they are all working on multiple projects where AKIDA technology has potential application.

FACT SEVEN

Quantum Ventura website contains the following information:

CyberNeuro-RT

An IoT, AI/ML-driven, highly-scalable, real-time network defense & threat intelligence tool with CPU, GPU or low-power neuromorphic chip deployment

A Quantum Ventura, Lockheed Martin, and Penn State Innovation

Quantum Ventura’s CyberNeuro-RT (CNRT) technology offering has been developed in partnership with Lockheed Martin Co.’s MFC Division and Pennsylvania State University under partial funding from the U.S. Department of Energy.

At-the-edge Neuromorphic Processing

• Two offerings from the leading neuromorphic developers: Intel and Brainchip
• Small form factor, magnitudes less power consumption than GPU
• On-chip learning for deployment network specific attack detection

Quantum Ventura Government Certifications and Partnerships link:

https://www.quantumventura.com/certifications-partnerships-consortiums