Dr E Brown
Regular
Sorry mate you're correct, I got it wrong. I did from shares held outside of super into SMSF. Thanks for the correction
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Pocketchange321
Member
Hi Dio, big fan of all your contributions to this forum.
Yes I know the pie stays the same, but what I mean is what you also pointed out, new shares issued to market to promote growth is something I've seen quite a few times in small cap pre revenue stocks, got burnt myself once.
However issued to cornerstone investor was not something I thought of, that could be very beneficial in the long run.
Existing shareholders increasing their holdings would still be dilution seen through my eyes, and requires purchasing power.
This can go down a number of ways, hopefully we know more come May 6th.
Pocketchange321
Member
What makes you think I don't have significant holdings? I've been promoting this stock shamelessly to everyone I meet, I got more than money riding on this.
Sorry it reads contradictory to you, was aiming for holistic in terms of good and bad.
I wrote reverse split INTO dilution. I know how it works, it's simple division, pie stays the same.
I can see you took my post quite personal, try not to do that on an anonymous stock forum where there are assholes with opposing views like me.
And yes I was still pumped when the share price fell to the 20c, cause what the share prices does until revenue is wholly insignificant, any solid move that will maintain it's highs will be backed by actual numbers.
Have a great day!
Fullmoonfever
Top 20
Nice to see a Microsoft AI Security Engineer recently list Akida as a notable neuromorphic chip 
We are # 3 below.
Only copied first half of post, rest on his LinkedIn.
www.linkedin.com
Sarthak Chaubey
+ Follow
This is where Neuromorphic Computing comes in—a revolutionary concept inspired by the human brain. Rather than relying on the rigid binary logic of conventional processors, neuromorphic systems mimic the brain’s structure and function. They are designed not just to compute, but to learn, adapt, and respond intelligently.
In this article, we’ll explore the principles, architecture, and real-world potential of neuromorphic computing. We’ll examine how it's transforming industries, overcoming limitations of traditional AI, and why it represents the next frontier in computing.
Unlike traditional von Neumann architectures, where memory and processing units are separate (causing bottlenecks), neuromorphic systems integrate these components more closely—just like the human brain.
We are # 3 below.
Only copied first half of post, rest on his LinkedIn.
Neuromorphic Computing: Building Brains, Not Machines
Introduction: A New Paradigm in Computing For decades, traditional computers have grown exponentially in power, speed, and capability, largely driven by Moore’s Law. But we are now approaching the physical and economic limits of silicon-based computing.
Neuromorphic Computing: Building Brains, Not Machines
Sarthak Chaubey
Sarthak Chaubey
Azure DevOps & AI Security Engineer at Microsoft…
Published Apr 11, 2025+ Follow
Introduction: A New Paradigm in Computing
For decades, traditional computers have grown exponentially in power, speed, and capability, largely driven by Moore’s Law. But we are now approaching the physical and economic limits of silicon-based computing. As our technological demands surge—particularly in fields like artificial intelligence (AI), robotics, and Internet of Things (IoT)—we need systems that can process information more efficiently, adapt to new environments, and function in real time.This is where Neuromorphic Computing comes in—a revolutionary concept inspired by the human brain. Rather than relying on the rigid binary logic of conventional processors, neuromorphic systems mimic the brain’s structure and function. They are designed not just to compute, but to learn, adapt, and respond intelligently.
In this article, we’ll explore the principles, architecture, and real-world potential of neuromorphic computing. We’ll examine how it's transforming industries, overcoming limitations of traditional AI, and why it represents the next frontier in computing.
1. What is Neuromorphic Computing?
Neuromorphic computing is a design philosophy and computational model that attempts to replicate the neural architecture and information-processing methods of the human brain using specialized hardware and software.Key Characteristics:
- Brain-inspired architecture: Mimics neurons, synapses, and the network connectivity of the brain.
- Event-driven processing: Uses spikes or events, similar to how neurons fire, to process information.
- Massive parallelism: Enables simultaneous processing across thousands or millions of neurons.
- Low power consumption: Mimics the brain’s remarkable energy efficiency (approx. 20W).
- On-chip learning: Supports real-time, unsupervised learning.
Unlike traditional von Neumann architectures, where memory and processing units are separate (causing bottlenecks), neuromorphic systems integrate these components more closely—just like the human brain.
3. A Brief History of Neuromorphic Engineering
Neuromorphic computing isn’t a brand-new concept—it has roots going back to the 1980s.Milestones:
- 1980s: Carver Mead, a pioneer in microelectronics at Caltech, coins the term “neuromorphic engineering.” He advocates building hardware that mimics neural systems.
- 1990s: Basic analog VLSI (Very Large Scale Integration) chips were developed to simulate neural behavior.
- 2000s: Progress in neuroscience and nanotechnology spurs interest. IBM launches Blue Brain and TrueNorth.
- 2010s: Intel introduces Loihi, a neuromorphic chip capable of learning on-chip. Universities like Stanford, MIT, and Heidelberg join the research.
- 2020s and Beyond: Major investments from DARPA, EU Human Brain Project, and companies like BrainChip and SynSense fuel rapid development.
4. Neuromorphic Hardware: Brains on a Chip
The true power of neuromorphic computing lies in its hardware. Several chips are now capable of mimicking synaptic learning and neural behavior.Notable Neuromorphic Chips:
1. IBM TrueNorth
- Contains 1 million neurons and 256 million synapses.
- Consumes just 70 milliwatts.
- Designed for pattern recognition and sensory processing.
2. Intel Loihi
- 128 neuromorphic cores with 130,000 neurons and 130 million synapses.
- Features on-chip learning, event-driven communication.
- Ideal for robotics, smart sensors, and adaptive control.
3. BrainChip Akida
- Focuses on edge AI applications (smart cameras, sensors).
- Offers real-time learning and low-power operation.
4. SpiNNaker (University of Manchester)
- Simulates spiking neural networks (SNNs) with a million processors.
- Used for brain simulations and neuroscience research.
Frangipani
Top 20
I don't want him to leave. However, if he does I hope he is not planning on leaving before his big gig in Santa Clara, CA at Embedded Vision Conference 2025. But this conference may also allow him and Ramesh an opportunity to get a room and formally consummate their relationship.
The Impact of More Efficient Training and Inferencing of Large Language Models on Edge AI Applications
Date: Wednesday, May 21
Start Time: 2:05 pm
End Time: 2:35 pm
The adoption of large language models (LLMs) and vision-language models (VLMs) in edge AI applications has been limited by on-device computation and memory resources, and also by the cost of training and developing large models suitable for edge applications. The adoption of new training methods and algorithms combined with innovative new model architectures promises to dramatically change the outlook for adoption with models created for the edge that can efficiently execute within SWaP and cost targets.
Track
Session Speakers
Steven Brightfield
Chief Marketing Officer, BrainChip
Steven Brightfield has over 20 years of success defining and bringing to market new semiconductor products with companies such as Qualcomm Technologies, SiMA.ai, LSI Logic, Plessey Semiconductors and Zoran for the mobile, AR/VR, wearable, edge ML, cable/sat set-top and digital camera markets. He has 10 years of experience launching programmable semiconductor IP cores for CPU/GPU/DSP/NPUs at LSI Logic, ARC International PLC (acquired by Synopsys), MIPS, Siliconarts, Improv Systems and BOPS and licensing these for use in end products that are ubiquitous today. Earlier, Steven’s technical foundation in digital signal processing led to using DSPs in innovative products that digitized the world of speech, audio, multimedia, graphics, camera and video processing. Steven recently joined the BrainChip leadership team to further drive BrainChip’s brand recognition, go-to-market strategy and customer acquisition as BrainChip enters a growth phase with its flagship Akida products. Steven has a BS in Electrical Engineering from Purdue University.
Cheers,
dippY
Hi @dippY22,
have you noticed that Steve Brightfield will no longer be presenting at the Embedded Vision Conference 2025 in Santa Clara on May 21?
At some point between between April 14 and April 24, his scheduled talk was quietly replaced with another one, which will be presented by our CTO Tony Lewis instead:
Of course this rescheduling of presentations may have happened for a totally different reason, but it sure feels like a déjà vu: last year, an Embedded Vision Summit presentation by our then-CMO Nandan Nayampally was quietly replaced by a talk that our then-Senior Product Manager Chris Jones gave (who has since also left our company), and soon after (on 2 May 2024 to be precise) we found out through a podcast (!) that Nandan Nayampally and Rob Telson had both left our company. I continue to believe that personal tensions were the real reason for their departure, not primarily the lure of a higher compensation package that BrainChip just couldn’t match (according to Sean Hehir in the case of our former CMO).
Temporal Event Neural Networks: A More Efficient Alternative to the Transformer - 2024 Summit
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional...
embeddedvisionsummit.com
White Horse
Regular
I think you will find the change in subject matter might be the reason.Hi @dippY22,
have you noticed that Steve Brightfield will no longer be presenting at the Embedded Vision Conference 2025 in Santa Clara on May 21?
At some point between between April 14 and April 24, his scheduled talk was quietly replaced with another one, which will be presented by our CTO Tony Lewis instead:
View attachment 83648
View attachment 83649
Of course this rescheduling of presentations may have happened for a totally different reason, but it sure feels like a déjà vu: last year, an Embedded Vision Summit presentation by our then-CMO Nandan Nayampally was quietly replaced by a talk that our then-Senior Product Manager Chris Jones gave (who has since also left our company), and soon after (on 2 May 2024 to be precise) we found out through a podcast (!) that Nandan Nayampally and Rob Telson had both left our company. I continue to believe that personal tensions were the real reason for their departure, not primarily the lure of a higher compensation package that BrainChip just couldn’t match (according to Sean Hehir in the case of our former CMO).
View attachment 83651
Temporal Event Neural Networks: A More Efficient Alternative to the Transformer - 2024 Summit
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional...
View attachment 83650
It's one thing to be conspiratorial, it's another to be paranoid.
Yes likely.
Steven B is still listed as an employee on BRN webpage as chief marketing officer, right next to Sean H.
I strongly disagree with your optimism...
BrainChip’s revenue remains negligible despite years of hype including supposed engagements with NASA, Mercedes, Ford, Valeo, and others EAPs... These "exciting" connections, even after developing new products based on apparent customers feedbacks, haven’t translated into meaningful sales. If we were truly engaged with multiple companies during the development, where’s the IP deals? Where's the revenue from engineering support or services? The financials show no traction, no one putting money on where their mouth is...
Management’s communication is abysmal - the abrupt redomiciling announcement with zero explanation is just the latest example.
We need a clear plan and answers on why sales keep failing. First contract in 2020, second in 2021, third in 2024—all for Akida 1.0 and no apparent royalties from the ones with MegaChips/Renesas. Why no material progress?
Shareholders deserve transparency and results, it's been years... not days or months or quarters...
I just hope they don't start talking about Akida 3 4 5 and start saying that the plan was to market them and was all expected and going well......
Dyor imo only
Pom down under
Top 20
They have there fingers in many pies.
Last edited:
Pom down under
Top 20
Way too long for me
Frangipani
Top 20
OHB Hellas are not only exploring Akida for their ‘Satellite as a Service’ concept (GIASAAS)
, but also as a consortium partner for an ESA project calledBOLERO (On-Board Continual Learning for SatCom Systems).
Prime contractor of the BOLERO project is KPLabs - both OHB Hellas and Eutelsat OneWeb are subcontractors.
BOLERO
Our mission is to connect Europe. We join engineers, entrepreneurs and investors to forge strong links between institutions, industries, and businesses.
BOLERO On-Board Continual Learning For SatCom Systems
- Next generation of systems
- Status
Ongoing - Status date
2025-05-01 - Activity Code
1B.138
The project identifies, explores, and implements onboard continual machine learning techniques to enhance reliability and data throughput of communication satellites.
The first objective is to identify 3 most promising use cases and applications of continual learning (CL), together with at least two most promising hardware platforms.
The second objective is to implement different CL techniques in the selected scenarios and assess their performance and feasibility for onboard deployment using the selected hardware platforms. The assessment includes the analysis of advantages and trade-offs of CL in comparison to traditional offline machine learning approaches, and the comparative analysis of hardware platforms for CL.
The final goal of the project is to identify a state-of-the-art, potential gaps, and future roadmap for CL in satellite communication systems.
Challenges
The main challenge is related to the limited resources and limited support for continual machine learning mechanisms in existing onboard processing units, e.g., not all operations and layers are supported and model parameters cannot be updated without hardware-specific recompilation. Therefore, common CL approaches are not straightforward to implement on board. Additionally, CL techniques come with the stability-plasticity trade-offs and the need of continuous validation and monitoring.
Benefits
The project offers a complete software and hardware pipeline to implement 3 different continual machine learning approaches (i.e., class-incremental, domain-incremental, and task-incremental) in 3 different application for communication satellites. The comparative analysis helps to identify which approach and hardware platform is best suited for different CL scenarios. The project establishes the foundation for future development of CL in SatCom systems.
Features
- Structured and informed report from selecting 3 most promising applications of CL in communication satellites and 2 hardware platforms
- Code for running a complete onboard CL process for both hardware platforms
- Report containing technology gaps and future roadmap for CL in SatCom systems
System Architecture
The 3 CL applications identified in the project are implemented for two hardware platforms of very different architectures (KP Labs Leopard DPU and BrainChip Akida neuromorphic computer). For each application and platform, there is a complete CL pipeline architecture proposed from data preprocessing to onboard continual learning.
Current status
The 3 most promising applications of continual machine learning in communication satellites have been identified, i.e., domain-incremental beam hopping optimization, task-incremental inter-satellite links routing, and class-incremental telemetry anomaly classification.
For each application, a state-of-the-art CL approach has been implemented for two diverse hardware platforms identified as the most promising ones for CL (KP Labs Leopard DPU and BrainChip Akida neuromorphic computer). The performance of each CL approach has been assessed and main technology gaps have been identified.
documentation
Documentation may be requestedPrime Contractor
KP Labs Sp. z o. o.
Polandhttps://kplabs.space
Subcontractors
OHB HELLAS
GreeceWebsite
Eutelsat OneWeb (OW)
United Kingdomhttps://oneweb.net/
Last update
2025-05-03 12:39
BOLERO: On-Board Continual Learning for SatCom Systems - [Article Title] | Explore KP Labs' Innovations and Insights
Discover insights from KP Labs in our news update, BOLERO: On-Board Continual Learning for SatCom Systems, focusing on key developments and innovations in satellite systems and space exploration.
www.kplabs.space
PROJECT
4 min read
BOLERO: On-Board Continual Learning for SatCom Systems
Published on
January 28, 2025
In an era of exponentially increasing data generation across all domains, satellite communications (SatCom) systems are no exception. The innovative BOLERO project, led by KP Labs, and supported by a consortium including OHB Hellas and Eutelsat OneWeb, is at the forefront of this technological evolution. This project is making significant strides in applying both classic and deep machine learning (ML and DL) techniques within the dynamic realm of satellite data, marking a transformative step in SatCom technology.
Understanding the Need for Continual Learning in SatCom
Traditionally, satellite applications have relied on supervised ML algorithms trained offline, with all training data prepared before the training process begins. This method is effective in stable data scenarios. For example, a deep learning model can accurately identify brain tumor lesions from magnetic resonance images after being trained on a diverse dataset. However, the dynamic space environment presents unique challenges. Factors such as thermal noise, atmospheric conditions, and on-board noise can significantly alter data characteristics, causing these offline-trained models to struggle or fail when encounteringnew, unfamiliar data distributions.The BOLERO Approach
BOLERO addresses these challenges by adopting an online training paradigm. The training process is shifted directly to the target environment, such as an edge device on a satellite. This innovative approach bypasses the need for downlinking large amounts of data for Earth-based retraining, overcoming bandwidth and time limitations. Training models in their deployment environment accelerate the training-to-deployment cycle and significantly improve model reliability under dynamic conditions.Tackling New Challenges
Implementing continual learning brings its own challenges, including catastrophic forgetting, where models may lose previously acquired knowledge. Additionally, the stability-plasticity dilemma must be addressed to ensure models are adaptable and capable of retaining learned information. BOLERO tackles these issues through strategies such as task-incremental learning, allowing models to adapt to new tasks, and domain-incremental learning, enabling them to handle data with evolving distributions.
The Consortium’s Collaborative Dynamics in BOLERO
The BOLERO project is propelled by the synergistic efforts of its consortium members. As the project leader, KP Labs is primarily responsible for developing the Synthetic Data Generators (SDGs) and the continual learning models, ensuring their efficacy across multiple SatCom applications and hardware architectures. OHB Hellas contributes by exploring novel machine learning methodologies suitable for streaming data, assessing continual learning applications in and beyond the space sector, and implementing two use cases in different hardware modalities. Eutelsat OneWeb focuses on identifying strategic space-based applications for continual learning, evaluating their business impact, and analyzing the benefits of continual learning models, particularly in terms of performance and cost-efficiency. Together, these entities combine their unique strengths to advance the BOLERO project, addressing the evolving demands of SatCom systems.
Real-World Applications and Future Impact
The applications of BOLERO are diverse, ranging from monitoring the operational capabilities of space devices to gas-level sensing and object detection in satellite imagery. These applications highlight the potential of continual learning to enhance the efficiency and accuracy of SatCom systems, potentially revolutionizing the management and processing of satellite data for more responsive, agile, and efficient operations.The BOLERO project, led by KP Labs and supported by a consortium including OHB Hellas and Eutelsat OneWeb, represents a groundbreaking step in harnessing the full potential of continual learning for SatCom systems. By confronting the unique challenges associated with satellite data and leveraging the latest in ML technology, BOLERO is poised to significantly improve the adaptability and efficiency of SatCom systems, setting a new standard in the field of satellite communications.
The other promising hardware platform being tested is KPLabs’ own Leopard DPU: https://www.kplabs.space/solutions/hardware/leopard
Frangipani
Top 20
OHB Hellas are not only exploring Akida for their ‘Satellite as a Service’ concept (GIASAAS)
BOLERO (On-Board Continual Learning for SatCom Systems).
Prime contractor of the BOLERO project is KPLabs - both OHB Hellas and Eutelsat OneWeb are subcontractors.
View attachment 83668
BOLERO
Our mission is to connect Europe. We join engineers, entrepreneurs and investors to forge strong links between institutions, industries, and businesses.connectivity.esa.int
BOLERO On-Board Continual Learning For SatCom Systems
Objectives
- Next generation of systems
- Status
Ongoing- Status date
2025-05-01- Activity Code
1B.138
The project identifies, explores, and implements onboard continual machine learning techniques to enhance reliability and data throughput of communication satellites.
The first objective is to identify 3 most promising use cases and applications of continual learning (CL), together with at least two most promising hardware platforms.
The second objective is to implement different CL techniques in the selected scenarios and assess their performance and feasibility for onboard deployment using the selected hardware platforms. The assessment includes the analysis of advantages and trade-offs of CL in comparison to traditional offline machine learning approaches, and the comparative analysis of hardware platforms for CL.
The final goal of the project is to identify a state-of-the-art, potential gaps, and future roadmap for CL in satellite communication systems.
Challenges
The main challenge is related to the limited resources and limited support for continual machine learning mechanisms in existing onboard processing units, e.g., not all operations and layers are supported and model parameters cannot be updated without hardware-specific recompilation. Therefore, common CL approaches are not straightforward to implement on board. Additionally, CL techniques come with the stability-plasticity trade-offs and the need of continuous validation and monitoring.
Benefits
The project offers a complete software and hardware pipeline to implement 3 different continual machine learning approaches (i.e., class-incremental, domain-incremental, and task-incremental) in 3 different application for communication satellites. The comparative analysis helps to identify which approach and hardware platform is best suited for different CL scenarios. The project establishes the foundation for future development of CL in SatCom systems.
Features
- Structured and informed report from selecting 3 most promising applications of CL in communication satellites and 2 hardware platforms
- Code for running a complete onboard CL process for both hardware platforms
- Report containing technology gaps and future roadmap for CL in SatCom systems
System Architecture
The 3 CL applications identified in the project are implemented for two hardware platforms of very different architectures (KP Labs Leopard DPU and BrainChip Akida neuromorphic computer). For each application and platform, there is a complete CL pipeline architecture proposed from data preprocessing to onboard continual learning.
Current status
The 3 most promising applications of continual machine learning in communication satellites have been identified, i.e., domain-incremental beam hopping optimization, task-incremental inter-satellite links routing, and class-incremental telemetry anomaly classification.
For each application, a state-of-the-art CL approach has been implemented for two diverse hardware platforms identified as the most promising ones for CL (KP Labs Leopard DPU and BrainChip Akida neuromorphic computer). The performance of each CL approach has been assessed and main technology gaps have been identified.
documentation
Documentation may be requested
Prime Contractor
KP Labs Sp. z o. o.
Poland
https://kplabs.space
Subcontractors
OHB HELLAS
Greece
Website
Eutelsat OneWeb (OW)
United Kingdom
https://oneweb.net/
Last update
2025-05-03 12:39
BOLERO: On-Board Continual Learning for SatCom Systems - [Article Title] | Explore KP Labs' Innovations and Insights
Discover insights from KP Labs in our news update, BOLERO: On-Board Continual Learning for SatCom Systems, focusing on key developments and innovations in satellite systems and space exploration.
PROJECT
4 min read
BOLERO: On-Board Continual Learning for SatCom Systems
Published on
January 28, 2025
In an era of exponentially increasing data generation across all domains, satellite communications (SatCom) systems are no exception. The innovative BOLERO project, led by KP Labs, and supported by a consortium including OHB Hellas and Eutelsat OneWeb, is at the forefront of this technological evolution. This project is making significant strides in applying both classic and deep machine learning (ML and DL) techniques within the dynamic realm of satellite data, marking a transformative step in SatCom technology.
Understanding the Need for Continual Learning in SatCom
Traditionally, satellite applications have relied on supervised ML algorithms trained offline, with all training data prepared before the training process begins. This method is effective in stable data scenarios. For example, a deep learning model can accurately identify brain tumor lesions from magnetic resonance images after being trained on a diverse dataset. However, the dynamic space environment presents unique challenges. Factors such as thermal noise, atmospheric conditions, and on-board noise can significantly alter data characteristics, causing these offline-trained models to struggle or fail when encounteringnew, unfamiliar data distributions.
The BOLERO Approach
BOLERO addresses these challenges by adopting an online training paradigm. The training process is shifted directly to the target environment, such as an edge device on a satellite. This innovative approach bypasses the need for downlinking large amounts of data for Earth-based retraining, overcoming bandwidth and time limitations. Training models in their deployment environment accelerate the training-to-deployment cycle and significantly improve model reliability under dynamic conditions.
Tackling New Challenges
Implementing continual learning brings its own challenges, including catastrophic forgetting, where models may lose previously acquired knowledge. Additionally, the stability-plasticity dilemma must be addressed to ensure models are adaptable and capable of retaining learned information. BOLERO tackles these issues through strategies such as task-incremental learning, allowing models to adapt to new tasks, and domain-incremental learning, enabling them to handle data with evolving distributions.
The Consortium’s Collaborative Dynamics in BOLERO
The BOLERO project is propelled by the synergistic efforts of its consortium members. As the project leader, KP Labs is primarily responsible for developing the Synthetic Data Generators (SDGs) and the continual learning models, ensuring their efficacy across multiple SatCom applications and hardware architectures. OHB Hellas contributes by exploring novel machine learning methodologies suitable for streaming data, assessing continual learning applications in and beyond the space sector, and implementing two use cases in different hardware modalities. Eutelsat OneWeb focuses on identifying strategic space-based applications for continual learning, evaluating their business impact, and analyzing the benefits of continual learning models, particularly in terms of performance and cost-efficiency. Together, these entities combine their unique strengths to advance the BOLERO project, addressing the evolving demands of SatCom systems.
Real-World Applications and Future Impact
The applications of BOLERO are diverse, ranging from monitoring the operational capabilities of space devices to gas-level sensing and object detection in satellite imagery. These applications highlight the potential of continual learning to enhance the efficiency and accuracy of SatCom systems, potentially revolutionizing the management and processing of satellite data for more responsive, agile, and efficient operations.
The BOLERO project, led by KP Labs and supported by a consortium including OHB Hellas and Eutelsat OneWeb, represents a groundbreaking step in harnessing the full potential of continual learning for SatCom systems. By confronting the unique challenges associated with satellite data and leveraging the latest in ML technology, BOLERO is poised to significantly improve the adaptability and efficiency of SatCom systems, setting a new standard in the field of satellite communications.
The other promising hardware platform being tested is KPLabs’ own Leopard DPU: https://www.kplabs.space/solutions/hardware/leopard
View attachment 83667
BOLERO – OHB-Hellas
www.ohb-hellas.gr
Because it would need to be like a 25-1 consolidation and they would issue a heap more shares at the same time to raise capital
Thankyou For you reply,
My shares are in fact in Australian Super also so I guess I’ll be selling them sooner rather than later.. at a loss unfortunately.
The AGM will hopefully shed some light on this 5 year plan and more detail on current engagements. No doubt it will be high jacked by questions on moving to the US to a degree but I sincerely hope that we are afforded the insight, without divulging detail, on where we “actually” stand in our commercial journey.
Partnerships are great but no business survives on minimal income and massive overheads forever.
Labsy
Regular
Anduril! Yeah! Was thinking the same.
For those who are not familiar, they should look this up. They just ousted Microsoft from defense for VR technology in defence amongst providing advanced drones etc etc ....
Space Cadet
Regular
My Super fund must be wrong then because I asked and they said I would have to sell them. I can't transfer them.
SC
Okeydokey01
Regular
I agree with you dippY22 that engagements along the lines of those you outline seem to be the most probable catalyst. For every line of reasoning I have gone down these continue to remain the most likely. Assuming the water tight NDA and obvious security requirements around these types of engagements I wouldn’t expect anything to be able to be released to market.
However perhaps this would be a time where actions could speak louder than words. Something along the lines of the board suspending performance bonuses and awarding of RSU's until revenue of $xxx was achieved.
If this move is indeed to satisfy the contractual requirements of one of these types of partners and we are that close. Then a pause in bonuses by the board, knowing what is coming would not be unreasonable, in my opinion. It would certainly speak volumes and go a long way to rebuilding trust for me.
Pom down under
Top 20
If we join as a 3rd tier company on the nasdaq then the requirement is $1 USD per share so 5-1 if the share price gets to 0.3 au. Information about 3rd tier supplied by other so can’t confirm if this information is correct. But still not good but loads better than 25-1. So maybe I good announcement with $$$ value then no consolidation I recon
HopalongPetrovski
I'm Spartacus!
What are the tiers of the Nasdaq market?
The Nasdaq Stock Market has three distinctive tiers: The Nasdaq Global Select Market®, The Nasdaq Global Market® and The Nasdaq Capital Market®. Applicants must satisfy certain financial, liquidity and corporate governance requirements to be approved for listing on any of these market tiers.
What Is the Nasdaq Capital Market?
The Nasdaq Capital Market is one of Nasdaq's U.S. market tiers containing early-stage companies that have relatively lower market capitalizations. Listing requirements for companies on the Nasdaq Capital Market are less stringent than for the two other Nasdaq market tiers, which focus on larger companies with higher market capitalization.KEY TAKEAWAYS
- The Nasdaq Capital Market (Nasdaq-CM) is one of three listing tiers on the Nasdaq exchange, specifically for companies that need to raise capital.
- Companies listed here may be small companies with a need to grow capital or shell corporations designed to raise capital in public markets for the purpose of acquiring other business entities.
- Companies that don't qualify for the Nasdaq National Market trade on Nasdaq-CM.
- Nasdaq Capital Market companies are required to meet a net income standard of at least $750,000, a minimum public float of 1,000,000 shares, at least 300 shareholders, and a share bid price of at least $4 (with certain exceptions).
Understanding the Nasdaq Capital Market
The Nasdaq Capital Market, known until 2005 as the Nasdaq SmallCap Market, primarily lists so-called small-cap stocks (typically those with market capitalizations of around $300 million to $2 billion). The name change reflected a shift in focus towards listing companies that need to raise capital. It's meant to be a less encumbered entrance for a smaller company or a Special Purpose Acquisition Company (SPAC) to capitalize and grow through a Nasdaq listing.12Although the initial listing requirements are relaxed, the corporate governancerequired to maintain a Nasdaq listing is the same across all tiers. This means Nasdaq Capital Market companies must have a code of conduct, an audit committee, independent directors, and so on.3
Listing Requirements for the Nasdaq Capital Market
The Nasdaq Capital Market makes it easier for early-stage companies to get listed, especially when compared to other senior exchanges with more onerous requirements. To list initially on the Nasdaq Capital Market, companies must meet all of the criteria under at least one of three listing standards—the equity standard, the market value of listed securities standard, or the net income standard.All the standards share some requirements such as one million publicly held shares, 300 shareholders, and three market makers (MMs). However, these also differ in important ways. The equity standard requires stockholders' equity of $5 million, where the other two require only $4 million, and it also requires an operating history of two years, while the other two do not require an operating history.
The market value of listed securities requires a market value of listed securities of $50 million and a market value of publicly held shares of $15 million. The net income standard is the only one requiring a net income, $750,000 in the latest fiscal year or in two of the last three years, but has the lowest requirement for market value of publicly held shares at $5 million.4
Nasdaq. “5500. The Nasdaq Capital Market.”
Although companies can pick the standard that best fits their situation, the listing standards and the required governance are more stringent than some early-phase capital markets. Because of the costs involved with meeting these standards, companies listing on the Nasdaq Capital Market often handily exceed the minimum requirements before they decide to list.5
Have you asked BRN whether they still have a commercial relationship with Marquee clients?
I have and a commercial relationship still exists..
They list the Marquee clients on the 'Why invest' part of the website and it has been pointed to them that if there is a change it must be reflected immediately for legal reasons.
Have you done some research about how long new and complex technology takes to be I produced into safety regulated autos and health?
Some posters use the word transperancy but never say precisely what it is.
Do you have a precise list of issues where you see there is a lack of transparency on? They cannot be dealt with unless precisely identified.
From time to time I ask questions for clarification and yes, I fish for Information on occasions like others do. Sometimes I get an answer and sometimes due to NDA's, competitive reasons etc I do not get a answer.
They did in fact provide their reasons for a redomicile in the Ann.
For perspective BRN is still a spec stock.
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