smoothsailing18
Regular
They mentioned the following which could be interesting
Renesas Electronics. Renesas is also a customer of EdgeCortix
Renesas Electronics. Renesas is also a customer of EdgeCortix
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
I guess if as expected Neuromorphic AI at the Edge experiences exponential growth and that BRN is currently a leader in the field then our patent value should be considerable.
It's likely that any new inventions that can perform the same tasks as Gen2/TENNs plus cover our roadmap could take years to prove out before being commercial.
See the table below showing the time it takes from invention to mass adoption. Timeframes are shortening but it looks like anything under 10 years for mass adoption would be seen as 'a rush'. Of course, there is some money to be made in the mid to later stages on the road to mass adoption.
The patent for AKIDA 1000 was granted in the US in October 2021 so mass adoption will not likely arrive until at the very least the 2030's.
Sean did say that the 'AI' revolution would eventually make the prior revolutions look like nothing.
Unfortunately it's just the way it is.
See the table below for estimates.
It took radio and TV at least three decades to go from invention to mainstream use. And once they proliferated, an entirely new sector—mass media—evolved, a key driver of globalization.
Year Invention Mass Adoption # of Years 1890s* Radio
1920 30 1920s* TV
1950 30 1970 Internet
1994 24 1971 1997 26 1973 Mobile Phones
2000 27 1994 Smartphones
2010 16 1996 USB
2005 9 2008 Blockchain
??? N/A 2017 Generative AI
??? N/A
EmailThe concept of neuromorphic computing was first coined in the 1980s by the scientist Carver Mead, who proposed creating electronic systems inspired by the neural structure of the human brain. This idea was based on the premise that the brain is, in essence, an extremely efficient and versatile information processor.
1980 ... 2025 45 years
Neuromorphic computing: merging artificial intelligence and the human brain
Neuromorphic computing: merging artificial intelligence and the human brain - Fundación Innovación Bankinter
Neuromorphic computing represents an exciting crossover between technology and biology, a frontier where computer science meets the mysteries of the human brain.
www.fundacionbankinter.org
DingoBorat
Slim
I'm not sure what everyone's problem is...
This is definitely OUR Century.
We just need some more patients.
On another note, has "anyone" heard "anything" from Zeeb0t lately?..
He seems to not be able to (or not wanting to) respond to issues that forum members are having, with the AVZ threads..
Do any of the Founding members, have direct contact with him, to find out what the issue is?
If "you" want, you can contact me on my outlook address.
This is definitely OUR Century.
We just need some more patients.
On another note, has "anyone" heard "anything" from Zeeb0t lately?..
He seems to not be able to (or not wanting to) respond to issues that forum members are having, with the AVZ threads..
Do any of the Founding members, have direct contact with him, to find out what the issue is?
If "you" want, you can contact me on my outlook address.
MDhere
Top 20
Morning Manny, be mindful that (We) that "some" came in around $1.50 -$2ish thanks to the Mercedes declaration which caused a big stir up $$.
What I am wishful for is that Mercedes being a customer on Brainchip webpage, is Mercedes AND BRAINCHIP make an announcement that also shows up in the asx and doesn't fade.
When I was overseas I was blown away by what was going on and it was this rush of buyers (of course because Mercedes announced their involvement
) So the (We) "some" may have invested on the back of one firework display.
I looking forward to the day those "some" can say yep (we) got in super early too
Rskiff
Regular
A thought that has been lingering in the back of my mind is with the share price being so low for so long, then is it to beneficial to BRN management to gain more shares as payment because of the price. ie if share price high less shares issued, thus low share price more issued to compensate. Surely when the next AGM votes on compensation then no more shares to be issued, instead they should be rewarded by share price increase (like us). They have plenty to make it worthwhile. Thoughts?
Pom down under
Top 20
Bravo
If ARM was an arm, BRN would be its biceps💪!
Nice!
May 31, 2025
Recent data reveals that real-time endpoint threat detection powered by AI-enhanced behavioral monitoring is becoming the cornerstone of modern cybersecurity strategies as organizations combat increasingly sophisticated threats targeting endpoint devices.
With the endpoint security market projected to reach USD 24.19 billion by 2029, security professionals are prioritizing solutions that can detect abnormal behaviors in real-time before breaches occur.
This growth reflects the urgent need for more sophisticated security measures as cyber threats evolve in complexity and scale.
“Organizations are completely reorienting their investment strategies, which has significant implications for large language model training, data deployment, and inference processes,” said Alex Michaels, Senior Principal Analyst at Gartner, during the recent Security & Risk Management Summit in Sydney.
This shift underscores the changing priorities in cybersecurity as AI technologies reshape defense mechanisms.
Research indicates that approximately 80% of successful cyber attacks utilize new and previously unidentified zero-day threats, making traditional signature-based detection insufficient for modern security needs.
This reality has accelerated the adoption of behavioral monitoring technologies that identify threats based on suspicious activities rather than known signatures.
This technology continuously tracks and analyzes user, application, and device behaviors across IT environments to identify deviations from established baselines of regular activity.
“By comparing observed behavior to known patterns of normal behavior, EDR solutions can identify deviations that may indicate the presence of malware or other malicious activity,” explains cybersecurity expert analysis from LinkedIn.
This approach enables organizations to detect and respond to threats that might remain undetected.
The technology employs real-time analytics to detect anomalies instantly, allowing organizations to identify and respond promptly to potential threats.
By constantly analyzing data from all endpoints, networks, and applications, behavioral monitoring systems can trace even slight changes in behavior that might quickly go unnoticed.
Behavior-based device-learning models in Microsoft Defender for Endpoint caught and stopped the attacker’s techniques at multiple points in the attack chain.
In another case, behavioral monitoring detected a privilege escalation activity involving a new variant of the notorious Juicy Potato hacking tool.
Minutes after the alert was triggered, the malicious file was analyzed and confirmed as malicious, and its process was stopped and blocked, preventing further attacks.
These examples illustrate how behavioral monitoring can detect threats early in the attack chain, providing critical time for security teams to respond before significant damage occurs.
“By definition, AI-based behavioral analytics provides real-time data on potentially malicious activity by identifying and acting on anomalies,” notes analysis from VentureBeat.
“Getting behavioral analytics right starts with behavioral machine learning models… trained on terabytes of high-resolution behavioral and contextual data.”
These technologies enable security systems to detect various threats, including malware, ransomware, and sophisticated attack techniques such as credential dumping, cross-process injection, and process hollowing.
The proliferation of IoT devices presents particular challenges, with research indicating that 96 percent of IT professionals acknowledge the necessity for more robust security strategies.
cybersecuritynews.com
ChatGPT 4.0
This article strongly reinforces the urgent market need for solutions like CyberNeuro-RT (CNRT) by outlining several critical trends and challenges that CNRT is uniquely positioned to address. Here’s how:
The article emphasizes that real-time, AI-enhanced behavioral monitoring is the new cornerstone of modern endpoint defense—exactly what CNRT is designed for. CNRT leverages BrainChip’s Akida neuromorphic processor, which is purpose-built for:
This directly matches the article’s conclusion that traditional, signature-based threat detection is no longer adequate in today’s threat landscape.
The projected growth from $18.7B in 2025 to $29.69B by 2029 (CAGR of 12.3%) validates a massive and growing commercial opportunity for CNRT.
The article states 80% of attacks now involve zero-day threats, which legacy systems can’t detect.
CNRT, powered by Akida, supports on-device learning and inference—so it can respond to never-before-seen patterns without needing cloud retraining or updates. This makes it a potent tool for real-time zero-day detection.
The article highlights behavioral systems like Microsoft Defender for Endpoint successfully halting major attacks using anomaly detection.
CNRT aims to do the same—but with ultra-low power and no dependency on high-performance GPUs or cloud services, which makes it better suited for:
This article doesn’t mention CNRT by name, but it presents a compelling case for exactly the kind of capabilities CNRT offers. The cybersecurity market is clearly moving toward real-time, AI-driven, low-latency, anomaly-based threat detection—and CNRT is a rare solution combining all of those with scalable, edge-compatible architecture.
In short: Yes, this article strongly supports the urgent need for CNRT in the market—and validates the direction BrainChip, Quantum Ventura, and Lockheed Martin have taken.
Behavioral Monitoring for Real-Time Endpoint Threat Detection
May 31, 2025
Recent data reveals that real-time endpoint threat detection powered by AI-enhanced behavioral monitoring is becoming the cornerstone of modern cybersecurity strategies as organizations combat increasingly sophisticated threats targeting endpoint devices.
With the endpoint security market projected to reach USD 24.19 billion by 2029, security professionals are prioritizing solutions that can detect abnormal behaviors in real-time before breaches occur.
Market Growth Signals Rising Threat Concerns
The endpoint security market is experiencing unprecedented growth. It was valued at USD 18.7 billion in 2025 and projected to reach USD 29.69 billion by 2029, growing at a compelling 12.3% CAGR.This growth reflects the urgent need for more sophisticated security measures as cyber threats evolve in complexity and scale.
“Organizations are completely reorienting their investment strategies, which has significant implications for large language model training, data deployment, and inference processes,” said Alex Michaels, Senior Principal Analyst at Gartner, during the recent Security & Risk Management Summit in Sydney.
This shift underscores the changing priorities in cybersecurity as AI technologies reshape defense mechanisms.
Research indicates that approximately 80% of successful cyber attacks utilize new and previously unidentified zero-day threats, making traditional signature-based detection insufficient for modern security needs.
This reality has accelerated the adoption of behavioral monitoring technologies that identify threats based on suspicious activities rather than known signatures.
How Behavioral Monitoring Works in Real-Time Defense
Behavioral monitoring represents a fundamental shift in cybersecurity, focusing on anomaly detection rather than signature matching.This technology continuously tracks and analyzes user, application, and device behaviors across IT environments to identify deviations from established baselines of regular activity.
“By comparing observed behavior to known patterns of normal behavior, EDR solutions can identify deviations that may indicate the presence of malware or other malicious activity,” explains cybersecurity expert analysis from LinkedIn.
This approach enables organizations to detect and respond to threats that might remain undetected.
The technology employs real-time analytics to detect anomalies instantly, allowing organizations to identify and respond promptly to potential threats.
By constantly analyzing data from all endpoints, networks, and applications, behavioral monitoring systems can trace even slight changes in behavior that might quickly go unnoticed.
Recent Success Stories Demonstrate Effectiveness
Microsoft recently reported that its behavioral blocking and containment capabilities successfully thwarted a credential theft attack targeting 100 organizations worldwide.Behavior-based device-learning models in Microsoft Defender for Endpoint caught and stopped the attacker’s techniques at multiple points in the attack chain.
In another case, behavioral monitoring detected a privilege escalation activity involving a new variant of the notorious Juicy Potato hacking tool.
Minutes after the alert was triggered, the malicious file was analyzed and confirmed as malicious, and its process was stopped and blocked, preventing further attacks.
These examples illustrate how behavioral monitoring can detect threats early in the attack chain, providing critical time for security teams to respond before significant damage occurs.
Integration with AI Accelerates Detection Capabilities
Integrating artificial intelligence and machine learning with behavioral analytics represents a significant advancement in endpoint security. AI algorithms are increasingly capable of establishing behavior baselines and identifying subtle deviations that could indicate compromise.“By definition, AI-based behavioral analytics provides real-time data on potentially malicious activity by identifying and acting on anomalies,” notes analysis from VentureBeat.
“Getting behavioral analytics right starts with behavioral machine learning models… trained on terabytes of high-resolution behavioral and contextual data.”
These technologies enable security systems to detect various threats, including malware, ransomware, and sophisticated attack techniques such as credential dumping, cross-process injection, and process hollowing.
Future Outlook for Endpoint Security
As organizations embrace remote work models and deploy more IoT devices, the endpoint security landscape will continue to evolve. Industry analysts predict continued growth in cloud-based endpoint security solutions, zero trust security models, and integrated security platforms.The proliferation of IoT devices presents particular challenges, with research indicating that 96 percent of IT professionals acknowledge the necessity for more robust security strategies.
Behavioral Monitoring for Real-Time Endpoint Threat Detection
AI-driven real-time endpoint threat detection is now key to modern cybersecurity against sophisticated device attacks.
cybersecuritynews.com
ChatGPT 4.0
This article strongly reinforces the urgent market need for solutions like CyberNeuro-RT (CNRT) by outlining several critical trends and challenges that CNRT is uniquely positioned to address. Here’s how:
Why CNRT Is Urgently Needed – Key Alignments with the Article
1. AI-Powered Behavioral Monitoring Is Now Essential
The article emphasizes that real-time, AI-enhanced behavioral monitoring is the new cornerstone of modern endpoint defense—exactly what CNRT is designed for. CNRT leverages BrainChip’s Akida neuromorphic processor, which is purpose-built for:
- Real-time processing at the edge
- Learning from new, never-before-seen threats (zero-day)
- Detecting anomalies in noisy, dynamic data environments
This directly matches the article’s conclusion that traditional, signature-based threat detection is no longer adequate in today’s threat landscape.
2. The Endpoint Security Market Is Booming
The projected growth from $18.7B in 2025 to $29.69B by 2029 (CAGR of 12.3%) validates a massive and growing commercial opportunity for CNRT.
- With AI-based endpoint solutions becoming a strategic investment focus, CNRT is perfectly timed for adoption.
- Its low-power edge processing differentiates it from cloud-reliant solutions, especially for latency-sensitive or remote environments (IoT, defense, etc.).
3. Rising Zero-Day Threats Demand On-Device Learning
The article states 80% of attacks now involve zero-day threats, which legacy systems can’t detect.
CNRT, powered by Akida, supports on-device learning and inference—so it can respond to never-before-seen patterns without needing cloud retraining or updates. This makes it a potent tool for real-time zero-day detection.
4. Proven Industry Use Cases Mirror CNRT’s Goals
The article highlights behavioral systems like Microsoft Defender for Endpoint successfully halting major attacks using anomaly detection.
CNRT aims to do the same—but with ultra-low power and no dependency on high-performance GPUs or cloud services, which makes it better suited for:
- Embedded systems
- Military or critical infrastructure
- Large-scale, decentralized environments
What Sets CNRT Apart
- Neuromorphic Advantage: Akida mimics brain-like processing, ideal for detecting patterns in complex, high-noise environments.
- Edge Deployment: No need to ship data to a central server; CNRT can act directly on-device.
- Low-Power Design: Unlike GPU-heavy solutions, CNRT is viable for IoT and mobile endpoints.
Conclusion
This article doesn’t mention CNRT by name, but it presents a compelling case for exactly the kind of capabilities CNRT offers. The cybersecurity market is clearly moving toward real-time, AI-driven, low-latency, anomaly-based threat detection—and CNRT is a rare solution combining all of those with scalable, edge-compatible architecture.
In short: Yes, this article strongly supports the urgent need for CNRT in the market—and validates the direction BrainChip, Quantum Ventura, and Lockheed Martin have taken.
Getupthere
Regular
The time frame from BrainChip (ASX: BRN) signing a customer for an IP license (e.g., for its Akida neuromorphic processor) to seeing a commercial product hit the market can vary significantly, but here’s a realistic breakdown:
General Timeline Estimate: 18 to 36 Months
Why the Range Varies
• Customer size: Large OEMs or Tier-1s often have longer, more complex product development cycles.
• End-use application: Consumer electronics may move faster (~18–24 months), while automotive or medical devices take longer due to regulatory hurdles (24–36+ months).
• Maturity of the customer’s design team: Startups may move quickly but face more technical roadblocks.
• Customization: If the customer modifies Akida significantly, it can extend the timeline.
Example Scenarios
• Smart camera using Akida for vision processing: ~18–24 months.
• Automotive ADAS chip using Akida: ~30–36+ months due to safety certifications (e.g., ISO 26262).
Summary
BrainChip’s IP model is long-cycle, typical of semiconductor licensing. From deal to revenue-generating product, expect 1.5 to 3 years, assuming standard SoC development timelines. Revenue may start flowing sooner through NRE (non-recurring engineering) fees or milestone payments, but volume-based royalties only arrive once products ship.
General Timeline Estimate: 18 to 36 Months| Stage | Description | Estimated Time |
| 1. IP Licensing Agreement Signed | Customer licenses Akida IP to embed into their own chip designs. | 0 months |
| 2. Integration & SoC Design | Customer integrates Akida IP into their system-on-chip (SoC). This includes RTL integration, simulation, verification. | 6–12 months |
| 3. Tape-Out & Fabrication | The SoC is finalized and sent to a foundry for fabrication (tape-out). | 3–6 months |
| 4. Silicon Validation & Testing | First silicon arrives and is tested extensively for bugs, power, performance. | 3–6 months |
| 5. Product Development | Chip is integrated into final product (e.g., a camera, sensor, wearable). Includes firmware/software development. | 3–6 months |
| 6. Certification & Mass Production | Compliance testing, certifications, manufacturing ramp. | 3–6 months |
| Total Time | From IP license to market launch. | ~18 to 36 months |
Why the Range Varies• Customer size: Large OEMs or Tier-1s often have longer, more complex product development cycles.
• End-use application: Consumer electronics may move faster (~18–24 months), while automotive or medical devices take longer due to regulatory hurdles (24–36+ months).
• Maturity of the customer’s design team: Startups may move quickly but face more technical roadblocks.
• Customization: If the customer modifies Akida significantly, it can extend the timeline.
Example Scenarios• Smart camera using Akida for vision processing: ~18–24 months.
• Automotive ADAS chip using Akida: ~30–36+ months due to safety certifications (e.g., ISO 26262).
SummaryBrainChip’s IP model is long-cycle, typical of semiconductor licensing. From deal to revenue-generating product, expect 1.5 to 3 years, assuming standard SoC development timelines. Revenue may start flowing sooner through NRE (non-recurring engineering) fees or milestone payments, but volume-based royalties only arrive once products ship.
Yes im here.
Yep, very quiet as nothings happened for ages. Would love to think it’s the calm before the storm, however I feel we’ll be sitting idle for a relatively long time yet. Revenue is 18 months overdue and that was my very conservative estimate!
I’m sitting on a decent stack (being diluted as each month passes) but management has not filled me with any confidence to buy any more at this stage. Gives me the absolute shits!
Same here. Accumulated heaps but sweet promises but nothing meaningful eventuated
smoothsailing18
Regular
Once quantum Venture releases their cybersecurity neuromorphic WiFi routers ect we should really see some $$$$ .
Hopefully this happens in 2025
Hopefully this happens in 2025
supersonic001
Regular
Will we see any revenue trickle through in Q3 as a result…..
If we’re in these things, how does it work, must there be a sale of product before royalties come good ? I’m curious on process if we’re in it at all….
If we’re in these things, how does it work, must there be a sale of product before royalties come good ? I’m curious on process if we’re in it at all….
JoMo68
Regular
I’m feeling pretty calm for what it’s worth. I’m confident we will have our time in the sun
CHIPS
Regular
To the best of my knowledge, MegaChip is Nintendo's supplier, and they already have an IP license.
smoothsailing18
Regular
Maybe quarterly for royalties
Baneino
Regular
The Nintendo Switch 2, which is scheduled for release on June 5, 2025, uses a specially developed Nvidia Tegra T239 processor (codenamed "Drake"). This chip is based on the Ampere architecture and has tensor cores that enable DLSS (Deep Learning Super Sampling). DLSS is an AI-powered upscaling technology from Nvidia that improves image quality without significantly impacting performance. I assume 99.9% that we are not part of it. I'm not saying that to make people sad, but to avoid drawing the wrong conclusions.
And a little piece of wisdom on the side: it takes at least 20 years of work for a company to become successful overnight.
Greetings from Germany
smoothsailing18
Regular
For the non technical are you saying that the new C button feature would be this Nivida architecture and have the ability of denoising that they are talking about ?.
Baneino
Regular
If you mean the new "C-buttons" or some sort of new input camera controls on the upcoming Nintendo Switch 2, there is speculation that Nintendo may be introducing new controller features or motion controls. However, this is not confirmed or I am not aware of anything. As for the noise reduction, as far as I know (would have to check again and o.icj have the source), a special Nvidia processor Tegra T239 is probably used. This chip also has
AI functions (tensor cores)
DLSS (Deep Learning Super Sampling) → improves graphics quality through AI
Possibility of noise suppression for audio/video (theoretically)
But I'm not quite sure if you mean this function.
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