Jacob Kiraz
Member
Its has been proven in the past Sean and Antonio have told a fib
BRN Discussion Ongoing
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Fullmoonfever
Top 20
The ASX code SCXT is likely a special cross trade for a specific security, where S indicates a special trade, C means cross trade, and XT specifies the type or tier of the special crossing. This type of trade, where a broker buys and sells the same security at the same time for different clients, can result in faster execution at competitive prices. Recent updates have simplified the reporting of special cross trades with the new code SC replacing older codes like S1, S2, and S3 for certain equity trades.
SCXT breakdown
- S: Indicates a "Special" trade, which might be a block trade or a crossing where pre-trade transparency rules are modified under specific conditions.
- C: Stands for "Cross" trade, where the broker executes both the buy and sell orders for the same security simultaneously for different clients.
- XT: Specifies the particular type or tier of the special cross trade.
Key features of special cross trades
- Faster execution: The trade may be executed faster than if sent to public trading venues, as it's an internal cross.
- Competitive pricing: The trade is executed at a price within the current National Best Bid and Offer (NBBO), meaning the client is not disadvantaged.
- Simplified reporting: The ASX has introduced a new SC condition code for special cross trades to simplify reporting, replacing older codes like S1, S2, and S3 for equity market products.
Aka: Thimble and pea ... The TaPXT
Someone was willing to take 44 million shares at 0.17. That means there is at least one big buyer who accepts the price at this level. That’s neutral to slightly positive rather than bearish… so far so good. But the share price still won’t change tomorrow because it was an off-market trade, most likely between institutional investors.
We still have to wait and see what announcements come from now on, and how strong they are – or not. That alone is what really matters for us.
Frangipani
Top 20
Bremen is the place to be this week when you want to engage with customers in the space tech business…
www.linkedin.com
Quick visit to Space Tech Expo in Bremen, meeting @Frontgrade Gaisler and our space customers who are looking forward to using GR801, a rad-hard fault tolerant Space SoC with integrated Akida IP:… | Alf Kuchenbuch
Quick visit to Space Tech Expo in Bremen, meeting @Frontgrade Gaisler and our space customers who are looking forward to using GR801, a rad-hard fault tolerant Space SoC with integrated Akida IP: https://lnkd.in/dMDV_-GU
Fullmoonfever
Top 20
Maybe not.
I wonder if Brokers can do a block trade made up of multiple parties on one or both sides of the trade. Most likely I suspect.
They ASX allows them to parcel up large blocks this way rather than smash or inflate the SP on mkt due to the volume of the trade.
You know.....orderly market n all that crap ...we wouldn't want to make them spend time like retailers drip feeding the buy / sells on mkt or have this create liquidity / volatility like the reason why bots and shorting are allowed....fuppets (fkn muppets).
I just found out that a firkin is 9 imperial gallons of ale, but 70 imperial gallons of wine - I've always preferred firkin wine.
Fiendish
Regular
Well.. somethings afoot! For good or ill soon we shall see!
Alf Kuchenbuch, BrainChip's VP of Sales for EMEA (the ASX-listed company behind the BRN.AX stock ticker), has been a key figure in pitching their neuromorphic AI tech—specifically the Akida IP—to the space sector. The "GRAIN" you're referring to isn't a literal grain but the GRAIN architecture, a new product line of radiation-hardened, space-grade system-on-chips (SoCs) developed by Frontgrade Gaisler in partnership with BrainChip. It integrates Akida for ultra-low-power, event-based AI processing tailored for satellites, deep-space probes, and autonomous missions (think real-time image recognition, navigation, and data analysis in orbit without draining batteries or failing under radiation).
Launched in April 2025, the first device in the line (GR801 SoC) combines Akida with Gaisler's NOEL-V RISC-V processor. It's designed for harsh space environments, enabling "always-on" AI that's 10-100x more energy-efficient than traditional chips. This is a big deal for BRN.AX because it positions BrainChip's IP as a go-to for the booming $10B+ space AI market, potentially driving licensing revenue and stock upside as adoption ramps.
As for how many space customers are "waiting" for GRAIN: There's no public exact tally (companies like BrainChip don't disclose pipeline specifics to avoid tipping competitors), but recent activities point to solid, growing demand from institutional players:
Confirmed contracts/partners: At least 2 major space agencies so far.
Swedish National Space Agency (SNSA): Awarded a commercialization contract in April 2025 to deploy GRAIN in Swedish-led missions, focusing on power-constrained satellites.
European Space Agency (ESA): BrainChip (with Frontgrade Gaisler, Airbus, and Neurobus) won ESA's "NEURAVIS" ITT (Invitation to Tender) in July 2024 for neuromorphic space tech evaluation, which directly feeds into GRAIN demos. ESA hosted the October 2025 EDHPC event where Alf demoed it.
Broader interest: Alf's been hustling at events, connecting with "space engineers and agencies" (plural, per BrainChip's Oct 2025 EDHPC recap). This includes hands-on demos for Earth observation, autonomous nav, and hazard detection. Sweden's Royal Institute of Technology (KTH) is already building a GRAIN demo app with neuromorphic sensors. The space crowd is hungry for this because traditional AI chips guzzle power and flop in radiation—GRAIN fixes that.
Right now (as of Nov 19, 2025), Alf's at the Space Tech Expo Europe in Bremen, Germany, schmoozing more prospects. Expect announcements on additional pilots or deals soon, as the line's still ramping to full production. For BRN.AX holders, this screams validation: space is a high-margin, sticky market, and GRAIN could be a multi-year revenue catalyst if it scales beyond these early wins. If you're trading on it, watch for Q4 updates from BrainChip's investor calls.
Alf Kuchenbuch, BrainChip's VP of Sales for EMEA (the ASX-listed company behind the BRN.AX stock ticker), has been a key figure in pitching their neuromorphic AI tech—specifically the Akida IP—to the space sector. The "GRAIN" you're referring to isn't a literal grain but the GRAIN architecture, a new product line of radiation-hardened, space-grade system-on-chips (SoCs) developed by Frontgrade Gaisler in partnership with BrainChip. It integrates Akida for ultra-low-power, event-based AI processing tailored for satellites, deep-space probes, and autonomous missions (think real-time image recognition, navigation, and data analysis in orbit without draining batteries or failing under radiation).
Launched in April 2025, the first device in the line (GR801 SoC) combines Akida with Gaisler's NOEL-V RISC-V processor. It's designed for harsh space environments, enabling "always-on" AI that's 10-100x more energy-efficient than traditional chips. This is a big deal for BRN.AX because it positions BrainChip's IP as a go-to for the booming $10B+ space AI market, potentially driving licensing revenue and stock upside as adoption ramps.
As for how many space customers are "waiting" for GRAIN: There's no public exact tally (companies like BrainChip don't disclose pipeline specifics to avoid tipping competitors), but recent activities point to solid, growing demand from institutional players:
Confirmed contracts/partners: At least 2 major space agencies so far.
Swedish National Space Agency (SNSA): Awarded a commercialization contract in April 2025 to deploy GRAIN in Swedish-led missions, focusing on power-constrained satellites.
European Space Agency (ESA): BrainChip (with Frontgrade Gaisler, Airbus, and Neurobus) won ESA's "NEURAVIS" ITT (Invitation to Tender) in July 2024 for neuromorphic space tech evaluation, which directly feeds into GRAIN demos. ESA hosted the October 2025 EDHPC event where Alf demoed it.
Broader interest: Alf's been hustling at events, connecting with "space engineers and agencies" (plural, per BrainChip's Oct 2025 EDHPC recap). This includes hands-on demos for Earth observation, autonomous nav, and hazard detection. Sweden's Royal Institute of Technology (KTH) is already building a GRAIN demo app with neuromorphic sensors. The space crowd is hungry for this because traditional AI chips guzzle power and flop in radiation—GRAIN fixes that.
Right now (as of Nov 19, 2025), Alf's at the Space Tech Expo Europe in Bremen, Germany, schmoozing more prospects. Expect announcements on additional pilots or deals soon, as the line's still ramping to full production. For BRN.AX holders, this screams validation: space is a high-margin, sticky market, and GRAIN could be a multi-year revenue catalyst if it scales beyond these early wins. If you're trading on it, watch for Q4 updates from BrainChip's investor calls.
Apologies if posted already
www.cio.com
www.linkedin.com
Neuromorphic computing and the future of edge AI
Forget quantum — neuromorphic AI could be the real disruptor, reshaping everything from medicine to warfare while cutting energy use dramatically.
www.cio.com
Neuromorphic computing and the future of edge AI | MYWAI
Neuromorphic computing isn’t just another buzzword—it’s a paradigm shift. By bringing intelligence closer to the edge, it enables faster, greener, and more resilient AI across critical domains. The AI killer app may still be emerging, but the trajectory is clear: AI that thinks locally, adapts...
Frangipani
Top 20
Bremen is the place to be this week when you want to engage with customers in the space tech business…
Quick visit to Space Tech Expo in Bremen, meeting @Frontgrade Gaisler and our space customers who are looking forward to using GR801, a rad-hard fault tolerant Space SoC with integrated Akida IP:… | Alf Kuchenbuch
Quick visit to Space Tech Expo in Bremen, meeting @Frontgrade Gaisler and our space customers who are looking forward to using GR801, a rad-hard fault tolerant Space SoC with integrated Akida IP: https://lnkd.in/dMDV_-GU
View attachment 93140
While Frontgrade Gaisler are expectedly spruiking GR801- their “rad hard, fault tolerant Space SoC with integrated Akida IP” - at Space Tech Expo Bremen…
#spacetechexpo2025 | Frontgrade Gaisler
We’re setting up the final touches in our booth at Space Tech Expo Europe 2025 in Bremen. Come by stand N31 in Hall 5 tomorrow to see the finished result! #SpaceTechExpo2025
(still picture taken from booth-setting-up video)
… they have also just signed an MoU with KP Labs from Poland “to join forces on next-generation on-board computing architectures that enhance autonomy and resilience for future space missions.” Their collaboration aims at the integration of FG’s GR716B with KP Labs’ Lion DPU.
Today, At Space Tech Expo Europe, we signed a Memorandum of Understanding with KP Labs to join forces on next-generation on-board computing architectures that enhance autonomy and resilience for… | Frontgrade Gaisler
Today, At Space Tech Expo Europe, we signed a Memorandum of Understanding with KP Labs to join forces on next-generation on-board computing architectures that enhance autonomy and resilience for future space missions. This collaboration aims at the integration of our GR716B rad-hard...
We know KP Labs as the prime contractor of the now concluded ESA BOLERO (On-Board Continual Learning for SatCom Systems) project, in which OHB Hellas and Eutelsat OneWeb were the subcontractors. That project involved another of KP Labs’ DPUs - the Leopard DPU.
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 called
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.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
View attachment 83667
The BOLERO project, which implemented and benchmarked continual machine learning techniques on “two hardware platforms of very different architectures” (namely KP Lab’s Leopard DPU and BrainChip’s Akida) gets a mention in a new LinkedIn video post by OHB Hellas, which introduces their Orbital High-Performance Computing (OHPC) team.
View attachment 91475
Giannis Panagiotopoulos, Space ML Engineer at OHB Hellas, describes the project as follows:
“[The] BOLERO project explores on-board continual machine learning techniques to enhance reliability and data throughput [in?] satellite communications systems. OHB Hellas has developed the continual learning models for two use cases and deployed them on selected hardware platforms, including a neuromorphic processing device.”
Orbital HPC - OHB Hellas | OHB Hellas
🚀 Introducing OHB Hellas! 🚀 In our second episode of the "GET TO KNOW OHB HELLAS" mini-series, meet the Orbital High-Performance Computing (OHPC) team. The Orbital HPC department is a dynamic and pioneering unit, specializing in advanced embedded systems engineering with a core focus on...
View attachment 91476
Is Akida possibly also being evaluated as part of the ongoing In-Space PoC-3 that the OHPC department is equally involved in (start date was 6 November 2024)?
OHB Hellas and their partners OHB Digital Connect and Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI, German Research Center for Artificial Intelligence) have been selected by ESA “to develop ideas for a networked fleet of autonomously operating in-space transportation vehicles.”
“In-Space Proof-of-Concept 3 is the third milestone in ESA’s in-space transportation roadmap, aiming at demonstrating the key enabling capabilities for on-board & shared intelligence, culminating at an In-Orbit Demonstration (IOD) of these capabilities.”
InSPoC-3 – OHB-Hellas
www.ohb-hellas.gr
View attachment 91479
In-Space Proof of Concept-3 (InSPoC-3): On-board and Shared Intelligence Ideation Activities - ESA Commercialisation Gateway
In-Space Proof-of-Concept 3 is the third milestone in ESA’s in-space transportation roadmap, aiming at demonstrating the key enabling capabilities for on-board & shared intelligence, culminating at an In-Orbit Demonstration (IOD) […]commercialisation.esa.int
View attachment 91480
View attachment 91481
View attachment 91482
The ESA project webpage on BOLERO (https://connectivity.esa.int/projects/bolero) gets updated from time to time, but still doesn’t tell us anything about the results of benchmarking the developed continual learning techniques on different hardware, that included KP Labs’ Leopard DPU as well as Akida:
“In BOLERO, we identified satcom functionalities that could benefit from on-board continual learning, selected them via a quantifiable process, and developed ML models accordingly.
We built data simulators to test various continual learning techniques, emphasizing reproducibility and algorithm generalization in realistic settings. The project delivered an end-to-end pipeline for continual learning and online adaptation for satcom, validated in simulated scenarios. Finally, we benchmarked these methods across different hardware, including KP Labs’ Leopard and BrainChip’s Akida, providing comprehensive results for all algorithms, hardware, and applications explored within BOLERO.”
Note that the ESA webpage lists the project status as “ongoing”, but hasn’t updated the header since July 2025. It does use past tense to describe the project, though.
On the OHB Hellas website (https://www.ohb-hellas.gr/ohb-hellas-project/bolero/), the project status is marked as “Delivered”.
The only article on BOLERO on KP Labs’ website so far is dated 28 January 2025 (https://www.kplabs.space/news/bolero-on-board-continual-learning-for-satcom-systems).
Is that from an article somewhere?
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