SharesForBrekky
Regular
How about Donald Trump and Xi Jinping. Now that would be an interesting conversation, haha.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Two guys representing Nvidia who apparently offered $2.99 USD a share and Sean can be seen here raising his arm/hand and
saying, "look you two, Tech has already said $4.99 a share, so take it or leave it"
rgupta
Regular
Yes hide akida 1 and akida 2 under akida 3 and we are ready to wait another 3 years, then comes akida 4
We are in love with akida family without caring we have no sales person to sell the technology.
Ha? Who said something like “we have to sell our products “? I think we should be self confident enough to wait until someone comes and buy our products?
COME TO BRAINCHIP
Last edited:
Taproot
Regular
NAVAIR
PMA-265
N202-091 - N202-122 = Naval Air Systems
N202-099
SEA-AIR-SPACE 2024 - NavySTP
The Navy SBIR Transition Program (Navy STP) is hosting a Navy Forum for STP Innovative Technology Showcase (Navy STP Showcase) booth at Sea-Air-Space 2024, the Navy League’s Global Maritime Exposition. We invite you to visit us at Booth 223 at the event scheduled for 8-10 April at the Gaylord...
navystp.com
Are BRE involved as well ? ( Blue Ridge Envisioneering )
BrainChip and Blue Ridge Envisioneering Collaborate on Next-Generation Tactical Edge Devices
Experience the collaboration between BrainChip and Blue Ridge Envisioneering for next-gen tactical edge devices.
brainchip.com
SBIR MENTAT
Definitive Contract N6833522C0158 worth up to $1.1M was awarded to Blue Ridge Envisioneering on 12/16/21 by the NAWC Aircraft Division (N68335-22-C-0158)
Award | SBIR
Award | SBIR
At some point the Boeing EA-18 Growler will incorporate BrainChip technology, and IMHO, that day is fast approaching.
Bring back Rob Telson. He got the job done
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 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
BOLERO – OHB-Hellas
www.ohb-hellas.gr
View attachment 83673 View attachment 83670 View attachment 83671
ESA’s BOLERO project webpage (BOLERO stands for On-Board Continual Learning For SatCom Systems) was updated earlier today:
BOLERO
Our mission is to connect Europe. We join engineers, entrepreneurs and investors to forge strong links between institutions, industries, and businesses.
“Current status
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.”
However, it appears none of the benchmark results have been published so far.
BOLERO On-Board COntinual LEarning FoR SatcOm Systems
Objectives
- FUTURE PREPARATION
- NEXT GENERATION OF SYSTEMS
- Status
Ongoing- Status date
2025-07-22- Activity Code
1B.138
The objectives of BOLERO were multi-fold:
Challenges
- To identify potential satellite telecommunication (satcom) functionality and applications that could benefit from the use of continual learning methodologies in a fully transparent, quantifiable and reproducible way which will be reusable in future satcom and other missions for an informed selection of on-board machine learning (ML) models that could benefit from continual learning techniques.
- To explore, develop, and simulate different continual learning implementation techniques for the identified satcom applications. This project objective also aimed to explore the connection between offline and online learning and the current state-of-the-art methodologies that would allow models that have been pre-trained offline to be updated so they can be enhanced by online/continual learning.
- To identify and justify a suitable system architecture for on-board continual learning applications through performing the benchmarking process of all developed ML algorithms with continual learning techniques for all satcom applications and simulation scenarios, as well as through performing the theoretical trade-off analysis of the hardware and system architectures considered in BOLERO.
The most important challenges of BOLERO related to:
Benefits
- Availability of hardware architectures that could be used for benchmarking continual learning AI algorithms.
- Availability of datasets that could be used to verify and validate continual learning scenarios (therefore, we developed synthetic data simulators for all selected satellite communications applications).
- Building reproducible, unbiased and reproducible pipelines for the quantitative validation of AI algorithms.
- Developing an objective procedure for selecting satcom use-cases that would benefit from continual learning paradigms.
The technology solutions develop in BOLERO bring important benefits that are transferable to the current and future (not only) satcom missions – these potential benefits include:
Features
- The possibility of synthesising the datasets in selected satcom applications (anomaly detection in telemetry data, congestion prediction with flexible payload, and inter-satellite link optimisation);
- Ready-to-use and thoroughly validated continual learning algorithms for satcom applications that can adapt their behavior to the changing data characteristics;
- The possibility of performing fully objective, quantifiable and reproducible analysis of (not only) satcom applications for on-board deployment in continual learning settings, and of hardware architectures that can be considered for on-board deployment in continual learning settings;
- The possibility of understanding the most pressing research and development gaps through analysing the developed research and development roadmaps that shall be followed to accelerate the adoption of continual learning in real-world satcom missions.
The BOLERO technology is composed of several pivotal components, including:
The roadmaps, presenting the most important activities that need to be followed to accelerate the adoption of continual learning technologies in satcom systems. These roadmaps have been split into those that relate to the algorithms, technologies, hardware as well as programmes, the latter indicating the programmatic gaps that were identified in this activity.
- An assessment matrix that can be used to objectively select the most appropriate satcom applications for on-board continual learning implementation (i.e., the use-cases and on-board applications that could benefit most from continual learning);
- An assessment matrix that can be used to quantify the applicability of the analysed hardware architectures in on-board implementation, with a special emphasis put on on-board continual learning algorithms;
- Synthetic data generators, developed for each considered satcom application (anomaly detection in satellite telemetry, beam-hopping, and inter-satellite routing) that can be used to synthetically generate data for simulated continual learning scenarios in a fully reproducible and traceable way;
- Continual learning AI algorithms for the selected satcom applications, dealing with the catastrophic forgetting phenomenon and addressing different continual learning strategies (including class-incremental learning, task-incremental learning, and domain-incremental learning).
System Architecture
The technology developed in BOLERO is fully modular, and directly relates to the key product features, including:
Plan
- Synthetic data generators;
- Continual learning artificial intelligence algorithms developed for selected satcom applications;
- Assessment matrices for selecting a) appropriate satcom applications for on-board continual learning deployment and the b) best hardware architectures for such on-board implementation;
- Research and development roadmaps. All these components are stand-alone and self-contained entities that can be effectively used separately.
Project was planned and divided into specific Work Packages focusing on the following:
- WP100 SOTA: Review and analysis;
- WP200 Satcom applications: Identification and analysis;
- WP300 Algorithms: Continual learning for satcom;
- WP400 Hardware: Performance assessment;
- WP500 Programmatic and development gaps;
- WP600 Software;
- WP700 Management, outreach and dissemination.
Current status
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.
Related links
KP Labs' blog post on the activity
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
2025-07-22 11:37
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.
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...
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.”
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) […]
I used to love listening to RT. He had a great confidence and way about him. Only thing I didn’t like was the bloody super hero question.Bring back Rob Telson. He got the job done
He brought enthusiasm, engaging language and knew how to conduct a well thought out interview.
I like many have been in this stock for 5+ years, some of you are double that. My patience is wearing a little thin after seeing other stocks rocket on news. We just don’t have any material news which saddens me.
Bring back $2.34 days, well even half of that and I’d be wrapped!
I’m still holding but it’s definitely been my most frustrating stock that I’ve ever held.
Best of luck to all of you!
Cheers
Andy
Frangipani
Top 20
In this 29 April article about the Future of Neuromorphic AI in Electronic Warfare, Steven Harbour not only confirms a partnership between Parallax Advanced Research and Intel (no surprise here, as he already used to collaborate with them closely for years while at SwRI), but also one between Parallax Advanced Research and BrainChip:
Parallax Advanced Research and the Future of Neuromorphic Artificial Intelligence in Electronic Warfare
Published on
Apr 29, 2025
The convergence of artificial intelligence and defense technologies is poised to redefine the future of electronic warfare (EW). This shift, driven by third-generation AI techniques like spiking neural networks (SNN) and neuromorphic research, represents a critical step forward in equipping the U.S. military with innovative and adaptable solutions. We spoke with Dr. Steven Harbour, Parallax Advanced Research director of AI Hardware Research and a leading expert in neuromorphic research, to explore how his team is advancing AI capabilities and addressing emerging challenges in defense.
Parallax Advanced Research and Southwest Research Institute (SwRI) EW Team; left to right: Mr. Justin S. Tieman, Principal Engineer, SwRI; Mr. Keith G. Dufford, Senior Program Manager, SwRI; Mr. David A. Brown, Institute Engineer; and Director AI Hardware Research and Neuromorphic Center of Excellence, Parallax; Dr. Steven D. Harbour
Exploring AI’s Next Frontier
Traditional AI excels in tasks it has been trained on, demonstrating precision in recognizing familiar patterns and processing expected queries. However, Harbour highlights a significant limitation: AI's brittleness when confronted with the unexpected.
Humans, on the other hand, adapt to the unknown through cognitive problem-solving, a capability that AI systems must emulate to address future challenges effectively.
SNNs, inspired by the human brain’s functionality, offer a promising solution. Unlike traditional feedforward neural networks rooted in inferential statistics, SNNs excel in rapid decision-making under uncertainty, making them particularly suited for dynamic environments like electronic warfare.
Scaling Neuromorphic Systems
Parallax is at the forefront of advancing third-generation AI algorithms, partnering with Intel and Brainchip to develop scalable neuromorphic hardware.
In terms of deployment, neuromorphic processors can be integrated into existing electronic countermeasure (ECM) pods, widely used in both Air Force and Navy operations. These pods, which are part of strike packages including crewed and uncrewed aircraft, offer a clear pathway for fielding these advanced systems across the Department of Defense (DoD).
The Role of Partnerships in Shaping AI Research
Collaboration plays a pivotal role in advancing neuromorphic research. Parallax, headquartered in Dayton, Ohio, benefits from proximity to leading institutions like the University of Dayton and the University of Cincinnati. Harbour’s connections with researchers like Professors Dr. Tarek Taha, Dr. Chris Yakopcic, and Dr. Vijayan K. Asari University of Dayton and Dr. Kelly Cohen an Endowed Chair and Lab Director at the University of Cincinnati have led to innovative projects, including combining “fuzzy” logic with Neuromorphic SNNs to enhance AI decision-making.
Parallax’s independent research efforts are further bolstered by partnerships with institutions like Intel and Brainchip, ensuring access to cutting-edge neuromorphic technologies. These collaborations not only drive technological innovation but also foster a thriving research ecosystem essential for addressing the unique challenges of EW.
Evolving Applications in Defense Technologies
Over the next few years, an AFLCMC initiative will focus on developing and deploying third-generation AI algorithms on neuromorphic platforms. According to Harbour, the initiative aims to create “fieldable systems that can operate effectively in air, sea, land, and space environments.” This vision extends to supporting broader DoD efforts, including AFRL’s test facilities and ongoing collaboration with Southwest Research Institute.
The adaptability of these systems will be critical for countering emerging threats. Harbour envisions a future where AI-powered EW solutions can address the unknown, enhancing situation awareness and enabling rapid response in high-stakes scenarios.
AI and the Future of EW
As neuromorphic research progresses, its impact on EW solutions for the U.S. military is undeniable. From enhancing strike packages to integrating AI into naval, land, and space operations, the potential applications are vast. Harbour emphasizes the importance of continued innovation and collaboration:
Through its pioneering work in AI and defense technologies, Parallax is shaping a future where adaptability and innovation are the cornerstones of national security. By bridging the gap between academic research and practical deployment, the team is ensuring that the U.S. military remains at the cutting edge of electronic warfare capabilities.
###
About Parallax Advanced Research & The Ohio Aerospace Institute (OAI)
Parallax is a 501(c)(3) private nonprofit research institute that tackles global challenges through strategic partnerships with government, industry, and academia. It accelerates innovation, addresses critical global issues, and develops groundbreaking ideas with its partners. With offices in Ohio and Virginia, Parallax aims to deliver new solutions and speed them to market. In 2023, Parallax and OAI formed a collaborative affiliation to drive innovation and technological advancements in Ohio and for the nation. OAI plays a pivotal role in advancing the aerospace industry in Ohio and the nation by fostering collaborations between universities, aerospace industries, and government organizations, and managing aerospace research, education, and workforce development projects.
Today, Parallax Advanced Research also posted on LinkedIn about “working with top partners like Intel, BrainChip, and Southwest Research Institute to build the next generation of adaptive, scalable defense systems”:
Parallax leads in neuromorphic AI for defense | Parallax Advanced Research posted on the topic | LinkedIn
🚀 Exploring the Future of AI in Electronic Warfare 🧠⚡ Parallax Advanced Research is breaking new ground in neuromorphic artificial intelligence, working with top partners like Intel, Brainchip, and Southwest Research Institute to build the next generation of adaptive, scalable defense systems...www.linkedin.com
View attachment 84011
https://parallaxresearch.org/news/blog/redefining-rf-perception-bio-inspired-leap-electronic-warfare
Sep 22, 2025
Parallax Advanced Research and the Ohio Aerospace Institute (OAI) are pioneering research that bridges biologically-inspired and unconventional computing, advanced sensing, and defense innovation. Dr. Steven D. Harbour, director of AI Hardware Research at Parallax/OAI and an adjunct professor at the University of Dayton, and his computing research team are advancing a new approach to electronic warfare (EW) perception. By fusing spiking neural networks, optical systems, and edge-AI hardware, their work promises to dramatically redefine RF situational awareness—moving beyond traditional signal processing to enable real-time, ultra-low-power cognition in highly contested electromagnetic environments. This research marks a bold step toward transforming how future battlefield systems detect, classify, and respond to RF threats at the tactical edge.
Caption: Dr. Steve Harbour, director of AI Hardware Research, Parallax Advanced Research and the Ohio Aerospace Institute
A Paradigm Shift in RF Awareness
At the core of this pioneering effort is Parallax’s novel use of direct (RF)1 to light conversion for signal classification. This achieved by an integrated photonic biologically-inspired RF pipeline that requires no digital pre-processing.
This innovation was born from a recognition that existing RF processing pipelines—reliant on frame-based digitization and compute-heavy Digital Signal Processing (DSP)2—collapse under low-probability-of-intercept (LPI) and jamming-heavy scenarios. Drawing inspiration from biological neural systems and event-driven vision, the team created a different path: fuse analog RF signals with optical biologically-inspired processing for resilient, edge-ready perception.
In this project, RF signals are not digitized or processed with conventional DSP. Instead, they are converted into optical spikes and processed with Spiking Neural Networks (SNNs)—a much more efficient, biologically inspired alternative suitable for edge deployment with low latency and power needs.
Operational and Strategic Impact
Unlike any existing architecture, it projects RF signals into the optical domain to generate event-based spikes. This allows direct classification using biologically-inspired SNNs, enabling ultra-low SWaP (Size, Weight, and Power) operation ideal for deployment on autonomous platforms.
Replacing frame-based RF capture with optical spike classification is more than just an efficiency upgrade—it’s a cognitive leap. For defense platforms, this means real-time detection, classification, and adaptive response to RF threats at the tactical edge, with no reliance on centralized compute. Commercially, the technology holds potential for secure IoT, spectrum monitoring, and next-generation cognitive radios.
“For the team, the most rewarding aspect has been seeing our idea recognized as a breakthrough,” said Dr. Harbour. “It’s a powerful reminder that the future of national security depends not just on better tools, but on entirely new ways of thinking.”
The team’s message to aspiring researchers: “Don’t just follow trends—combine disparate domains like physics and AI in unexpected ways. Align with national security missions to transform, not just improve.”
---
Key Terms:
1 "RF" refers to the range of electromagnetic frequencies used for wireless communication, radar, and sensing.
2 "DSP" refers to the Digital Signal Processing techniques used to analyze and manipulate RF signals once they’ve been captured. In a traditional RF system, the analog signal is sampled and digitized at high rates using an analog-to-digital converter (ADC) and processed using DSP algorithms to extract meaningful features (e.g., frequency, modulation type, signal classification). These DSP chains are computationally intensive and typically require substantial power and processing infrastructure, which is problematic for low-SWaP (Size, Weight, and Power) applications like drones or distributed sensors.
###
About Parallax Advanced Research & The Ohio Aerospace Institute
Parallax is a research institute that tackles global challenges through strategic partnerships with government, industry, and academia. It accelerates innovation, addresses critical global issues, and develops groundbreaking ideas with its partners. With offices in Ohio and Virginia, Parallax aims to deliver new solutions and speed them to market. In 2023, Parallax and OAI formed a collaborative affiliation to drive innovation and technological advancements in Ohio and for the nation. OAI plays a pivotal role in advancing the aerospace industry in Ohio and the nation by fostering collaborations between universities, aerospace industries, and government organizations, and managing aerospace research, education, and workforce development projects.
LINK
Attachments
Last edited:
Well said Andy...it's been a real journey to date, I'm over double your time holding and a number are beyond me,
but one thing I have never lost sight of is the technology, which has progressed and become more defined than
ever, and the fact that Peter and Anil still are solid holders of the stock, yes, they are a lot more financially secure than
the average individual but, they know what we have, and it's truly brilliant, world class, just ask NASA or Raytheon etc,
our time will come, Quantum Computing, Photonic Computing are the next cabs off the rank in my opinion, but history
tells us, it's one thing at a time, humanity has enough trouble dealing with selfishness as it is, let alone Mr. AGI
Thanks Frangipani, from the article link you quoted:https://parallaxresearch.org/news/blog/redefining-rf-perception-bio-inspired-leap-electronic-warfare
Sep 22, 2025
View attachment 91483
Parallax Advanced Research and the Ohio Aerospace Institute (OAI) are pioneering research that bridges biologically-inspired and unconventional computing, advanced sensing, and defense innovation. Dr. Steven D. Harbour, director of AI Hardware Research at Parallax/OAI and an adjunct professor at the University of Dayton, and his computing research team are advancing a new approach to electronic warfare (EW) perception. By fusing spiking neural networks, optical systems, and edge-AI hardware, their work promises to dramatically redefine RF situational awareness—moving beyond traditional signal processing to enable real-time, ultra-low-power cognition in highly contested electromagnetic environments. This research marks a bold step toward transforming how future battlefield systems detect, classify, and respond to RF threats at the tactical edge.
Caption: Dr. Steve Harbour, director of AI Hardware Research, Parallax Advanced Research and the Ohio Aerospace Institute
A Paradigm Shift in RF Awareness
At the core of this pioneering effort is Parallax’s novel use of direct (RF)1 to light conversion for signal classification. This achieved by an integrated photonic biologically-inspired RF pipeline that requires no digital pre-processing.
This innovation was born from a recognition that existing RF processing pipelines—reliant on frame-based digitization and compute-heavy Digital Signal Processing (DSP)2—collapse under low-probability-of-intercept (LPI) and jamming-heavy scenarios. Drawing inspiration from biological neural systems and event-driven vision, the team created a different path: fuse analog RF signals with optical biologically-inspired processing for resilient, edge-ready perception.
In this project, RF signals are not digitized or processed with conventional DSP. Instead, they are converted into optical spikes and processed with Spiking Neural Networks (SNNs)—a much more efficient, biologically inspired alternative suitable for edge deployment with low latency and power needs.
Operational and Strategic Impact
Unlike any existing architecture, it projects RF signals into the optical domain to generate event-based spikes. This allows direct classification using biologically-inspired SNNs, enabling ultra-low SWaP (Size, Weight, and Power) operation ideal for deployment on autonomous platforms.
Replacing frame-based RF capture with optical spike classification is more than just an efficiency upgrade—it’s a cognitive leap. For defense platforms, this means real-time detection, classification, and adaptive response to RF threats at the tactical edge, with no reliance on centralized compute. Commercially, the technology holds potential for secure IoT, spectrum monitoring, and next-generation cognitive radios.
“For the team, the most rewarding aspect has been seeing our idea recognized as a breakthrough,” said Dr. Harbour. “It’s a powerful reminder that the future of national security depends not just on better tools, but on entirely new ways of thinking.”
The team’s message to aspiring researchers: “Don’t just follow trends—combine disparate domains like physics and AI in unexpected ways. Align with national security missions to transform, not just improve.”
---
Key Terms:
1 "RF" refers to the range of electromagnetic frequencies used for wireless communication, radar, and sensing.
2 "DSP" refers to the Digital Signal Processing techniques used to analyze and manipulate RF signals once they’ve been captured. In a traditional RF system, the analog signal is sampled and digitized at high rates using an analog-to-digital converter (ADC) and processed using DSP algorithms to extract meaningful features (e.g., frequency, modulation type, signal classification). These DSP chains are computationally intensive and typically require substantial power and processing infrastructure, which is problematic for low-SWaP (Size, Weight, and Power) applications like drones or distributed sensors.
###
About Parallax Advanced Research & The Ohio Aerospace Institute
Parallax is a research institute that tackles global challenges through strategic partnerships with government, industry, and academia. It accelerates innovation, addresses critical global issues, and develops groundbreaking ideas with its partners. With offices in Ohio and Virginia, Parallax aims to deliver new solutions and speed them to market. In 2023, Parallax and OAI formed a collaborative affiliation to drive innovation and technological advancements in Ohio and for the nation. OAI plays a pivotal role in advancing the aerospace industry in Ohio and the nation by fostering collaborations between universities, aerospace industries, and government organizations, and managing aerospace research, education, and workforce development projects.
LINK
Scaling Neuromorphic Systems
Parallax is at the forefront of advancing third-generation AI algorithms, partnering with Intel and Brainchip to develop scalable neuromorphic hardware. "" Harbour says, “Both Intel’s Loihi and Brainchip’s hardware appears plausibly scalable for platforms like fighter aircraft or drones.” "
" Parallax’s independent research efforts are further bolstered by partnerships with institutions like Intel and Brainchip, ensuring access to cutting-edge neuromorphic technologies. These collaborations not only drive technological innovation but also foster a thriving research ecosystem essential for addressing the unique challenges of EW."
Frangipani
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#gsa #whereleadersmeet #semiconductor #technology #ai #globalcollaboration | Global Semiconductor Alliance
That’s officially a wrap on the 2025 GSA U.S. Executive Forum!💥 This afternoon brought together some of the most powerful voices shaping the semiconductor industry — visionaries, innovators, and executives who are leading the charge in technology, growth, and global impact. A special thank you...
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#brainchip #akida #neuromorphicai #gsa #executiveforum #semiconductors #edgeai #innovation | BrainChip
We’re proud to have attended and sponsored the GSA U.S. Executive Forum 2025 🌎✨ It was a great opportunity to connect with leaders across the semiconductor industry and highlight how BrainChip’s Akida technology is shaping the future of intelligent computing at the edge 🚀 👏 A special thank you...
Frangipani
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Beyond Mechanistic Control: Causal Decision Processing in Neuromorphic Military Artificial | Institute for National Strategic Studies
How can AI designed to mimic the human brain change the future of military decision-making? This week's Strategic Insights article by Dr. James Giordano explores neuromorphic AI and its ability to make more adaptive, context-sensitive decisions by going beyond rigid “if-then” programming...
“Conclusion
The concepts of expanded causal frameworks provide key insights for understanding and implementing neuromorphically-base AI systems that can be employed in military contexts. Without doubt, these systems offer considerable capabilities for adaptive, context-sensitive decision-making; yet they also challenge undergirding assumptions about predictability, control, and accountability that are essential to traditional military doctrine.Therefore, I posit that success in integrating these technologies will require knowledge, appreciation, and engagement of technical advancement as well as modification of military thinking, training, and oversight mechanisms that will enable effective, efficient and ethically sound operational use of such systems. Simply put, I believe that the stakes are far too high for anything less than engaging a careful, systematic approach to understanding and managing these emerging capabilities. With the integration of iteratively autonomous AI systems in military contexts, I offer that lessons from the brain sciences about the complexity of causal decisional and action processes in neural systems should inform and guide prudent approaches to neuromorphically-based artificial ones.”
Dr. James Giordano is Director of the Center for Disruptive Technology and Future Warfare of the Institute for National Strategic Studies at the National Defense University.
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Frangipani
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While that’s true, the question is:
Doesn’t it defeat the actual purpose why we got partnered in the first place, when developers can no longer train new models on Akida?!
In yesterday’s LinkedIn post, Edge Impulse started out by saying:
#imagine2025 #edgeai #brainchip #akida #edgeimpulse | Edge Impulse (a Qualcomm company)
🧠 BrainChip is uniquely integrated with Edge Impulse to train models for their Akida platform. At Imagine 2025, BrainChip will showcase: -Edge Impulse out-of-the-box demo models running on the Akida AKD1500 and Akida FPGA development platforms -One-of-a-kind models developed by BrainChip’s...
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Yet, when interested developers then visit the Edge Impulse/Ecosystem-Partners/BrainChip webpage you shared earlier (which I personally find appealing, by the way) and then click on “BrainChip Docs” under “RESOURCES”…
BrainChip
Essential AI. Ubiquitous, distributed AI. Close to the sensor. Inspired by the human brain. Enabled by us.edgeimpulse.com
… this is how they will be greeted:
BrainChip AKD1000 - Edge Impulse Documentation
docs.edgeimpulse.com
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That is very unprofessional and should have been rectified ages ago - I recall that somebody even addressed this issue during the AGM in May, to which our management replied they weren’t aware of any problem with model training on Edge Impulse.
Although a month earlier, @Smoothsailing / @smoothsailing18 had already asked IR for clarification on this issue and got a reply from Tony Dawe that our CTO Tony Lewis were not concerned and had instead referred to the suspension as a “temporary situation” stemming from the acquisition, since Qualcomm had to “review all contracts and commercial arrangements”. (https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-457138)
Or else, if model training really DOES continue to be suspended for whatever reason, they should stop with the misleading message that developers can train models for their Akida platform on Edge Impulse, as it currently only concerns those developers who already have “existing trained Edge Impulse projects to deploy to BrainChip devices”.
Either way not a good look.
Looks like after almost six months, the issue with not being able to train new Akida models on Edge Impulse has finally been resolved!
BrainChip AKD1000 - Edge Impulse Documentation
docs.edgeimpulse.com
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