FiveBucks
Regular
Sony is working with AMD to integrate CNN AI learning into future games/consoles.
What's wrong with SNN Sony???
What's wrong with SNN Sony???
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
PROPHESEE | Metavision Technologies
REVEAL THE INVISIBLE with the world's most advanced neuromorphic vision system, inspired by human vision and built on the foundation of neuromorphic engineering. PROPHESEE gives Metavision to machines, revealing what was previously invisible to them.
www.prophesee.ai
What are prophesee currently using that allows 2mW sensing power consumption?
Also good to see they have brainchip listed as a partner. Synsense who they have previously partnered with arent listed
DingoBorat
Slim
There will 100% NOT be another IP announcement, this Friday morning.
Tothemoon24
Top 20
Visiting BrainChip ’s offices in #LagunaHills, California, and meeting M Anthony Lewis was an incredible experience. Our conversation provided unique insights and inspiration, further enriching my understanding of where technology is heading. Moments like these are invaluable to Robotic Crew , WeDevelop and my self in the journey of shaping the future. Thank you, Anthony, for sharing your time and vision!
Tothemoon24
Top 20
It’s going to be one big celebration 
when our friends from Tata put pen to paper , cmon Santa 
when our friends from Tata put pen to paper , cmon Santa
Tothemoon24
Top 20
Tuliptrader
Regular
Could this possibly be somewhere that Akida TENN's could shine?It’s going to be one big celebration
View attachment 74481
View attachment 74482
TT
KP Labs – A new chapter in on-board data processing
KP Labs provides advanced hardware, software, and data processing solutions for space exploration, revolutionizing spacecraft operations since 2016.
www.kplabs.space
KP Labs on LinkedIn: #kplabs #missioncomplete #website
"A New Chapter in On-board Data Processing - End-to-End Hardware, Software, and Algorithm Solutions for SmallSats." 🌟 That’s our new claim, and it…
HopalongPetrovski
I'm Spartacus!
Dallas
Regular
Das Folgende stammt von Grok ...
Ich habe gefragt, wie neuromorphes Computing das Quantencomputing ergänzen kann …
Neuromorphes Computing und Quantencomputing sind beides aufstrebende Bereiche, die darauf abzielen, die Grenzen traditioneller Computerarchitekturen zu überwinden. Hier sind einige Gründe, warum sie sich gut miteinander integrieren lassen:
Ergänzende Computerparadigmen:
Neuromorphes Computing: Ahmt die neuronale Struktur des menschlichen Gehirns nach und konzentriert sich dabei auf parallele Verarbeitung, geringen Stromverbrauch und adaptives Lernen durch pulsierende neuronale Netzwerke. Dieses Paradigma eignet sich hervorragend für Aufgaben, die Mustererkennung, Lernen aus unstrukturierten Daten und Echtzeitverarbeitung beinhalten.
Quantencomputing: Nutzt quantenmechanische Phänomene wie Superposition und Verschränkung, um Berechnungen durchzuführen, die bestimmte Probleme exponentiell schneller lösen können als klassische Computer, insbesondere in Bereichen wie Optimierung, Kryptographie und Quantensimulation.
Integration: Die Stärken des neuromorphen Computings im Umgang mit komplexen, unstrukturierten Daten und Lernprozessen könnten die Fähigkeit des Quantencomputings ergänzen, Probleme mit riesigen Lösungsräumen oder bei denen klassische Algorithmen aufgrund der Rechenkomplexität versagen, zu bewältigen und zu optimieren. Dies könnte zu hybriden Systemen führen, bei denen die neuromorphe Verarbeitung die Dateneingabe/-ausgabe und das Lernen verwaltet, während das Quantencomputing bestimmte Rechenaufgaben wie Optimierung oder Simulation übernimmt.
Energieeffizienz:
Neuromorphe Systeme sind so konzipiert, dass sie im Vergleich zu herkömmlichen Computern energieeffizienter sind. Dies ist ein erheblicher Vorteil, da Quantencomputer derzeit eine extreme Kühlung auf Temperaturen nahe dem absoluten Nullpunkt erfordern. Die Integration neuromorpher Elemente könnte dazu beitragen, den Energiebedarf eines Hybridsystems zu decken, wodurch Quantencomputeroperationen möglicherweise für breitere Anwendungen praktischer werden.
Umgang mit verrauschten Daten:
Beide Systeme können auf unterschiedliche Weise mit verrauschten oder unvollständigen Daten umgehen. Neuromorphes Computing kann lernen und sich an Datenrauschen anpassen, ähnlich wie biologische Neuronen funktionieren, während Quantencomputing nachweislich Rauschen in bestimmten Algorithmen ausnutzt, um einen Quantenvorteil zu erzielen. Diese gemeinsame Fähigkeit könnte in Hybridsystemen synergisiert werden, um die Robustheit gegenüber Fehlern oder Rauschen in realen Anwendungen zu verbessern.
Quantenneuromorphe Hardware:
Die Forschung hat das Konzept von Quantenmemristoren untersucht, die als Grundlage für Quantenneuronale Netzwerke dienen könnten. Diese Geräte könnten Quantenversionen neuromorpher Architekturen realisieren und möglicherweise die Rechenleistung beider Paradigmen verbessern, indem sie quantenverstärkte neuronale Verarbeitungs- oder Lernalgorithmen ermöglichen.
Skalierbarkeit und anwendungsspezifische Lösungen:
Neuromorphe Systeme können durch einfaches Hinzufügen weiterer Chips oder Neuronen skaliert werden, im Gegensatz zu Quantensystemen, bei denen die Skalierung derzeit aufgrund von Kohärenz und Fehlerraten mit erheblichen Herausforderungen verbunden ist. Zusammen könnten sie jedoch maßgeschneiderte Lösungen bieten, bei denen die Quantenverarbeitung für bestimmte rechenintensive Aufgaben innerhalb eines größeren neuromorphen Rahmens verwendet wird, der Daten und Lernen verwaltet, und so eine skalierbare Architektur für die Lösung komplexer Probleme schafft.
Maschinelles Lernen und KI-Beschleunigung:
Quantencomputing hat das Potenzial, Algorithmen des maschinellen Lernens zu beschleunigen, insbesondere in Bereichen wie quantengestütztes maschinelles Lernen oder Quantenreservoir-Computing. Neuromorphes Computing könnte hierzu beitragen, indem es eine Plattform für Echtzeit-Datenverarbeitung und Lernen am Rande der Algorithmen bietet, wobei Quantencomputing Rechenleistungssteigerungen für Trainings- oder Optimierungsphasen bietet.
Zusammenfassend lässt sich sagen, dass die Integration neuromorpher Technologien in das Quantencomputing den Weg für eine neue Klasse von Computersystemen ebnen könnte, die die Stärken beider Technologien nutzen, um Probleme effizienter und mit geringerem Energieverbrauch zu lösen und ein breiteres Anwendungsspektrum zu bieten, insbesondere in den Bereichen künstliche Intelligenz, Datenanalyse und Optimierung komplexer Systeme.https://x.com/QAN_Future/status/1869515238829162980?t=NqwsLXDIOf2Ky7DntVgrTw&s=19
Ich habe gefragt, wie neuromorphes Computing das Quantencomputing ergänzen kann …
Neuromorphes Computing und Quantencomputing sind beides aufstrebende Bereiche, die darauf abzielen, die Grenzen traditioneller Computerarchitekturen zu überwinden. Hier sind einige Gründe, warum sie sich gut miteinander integrieren lassen:
Ergänzende Computerparadigmen:
Neuromorphes Computing: Ahmt die neuronale Struktur des menschlichen Gehirns nach und konzentriert sich dabei auf parallele Verarbeitung, geringen Stromverbrauch und adaptives Lernen durch pulsierende neuronale Netzwerke. Dieses Paradigma eignet sich hervorragend für Aufgaben, die Mustererkennung, Lernen aus unstrukturierten Daten und Echtzeitverarbeitung beinhalten.
Quantencomputing: Nutzt quantenmechanische Phänomene wie Superposition und Verschränkung, um Berechnungen durchzuführen, die bestimmte Probleme exponentiell schneller lösen können als klassische Computer, insbesondere in Bereichen wie Optimierung, Kryptographie und Quantensimulation.
Integration: Die Stärken des neuromorphen Computings im Umgang mit komplexen, unstrukturierten Daten und Lernprozessen könnten die Fähigkeit des Quantencomputings ergänzen, Probleme mit riesigen Lösungsräumen oder bei denen klassische Algorithmen aufgrund der Rechenkomplexität versagen, zu bewältigen und zu optimieren. Dies könnte zu hybriden Systemen führen, bei denen die neuromorphe Verarbeitung die Dateneingabe/-ausgabe und das Lernen verwaltet, während das Quantencomputing bestimmte Rechenaufgaben wie Optimierung oder Simulation übernimmt.
Energieeffizienz:
Neuromorphe Systeme sind so konzipiert, dass sie im Vergleich zu herkömmlichen Computern energieeffizienter sind. Dies ist ein erheblicher Vorteil, da Quantencomputer derzeit eine extreme Kühlung auf Temperaturen nahe dem absoluten Nullpunkt erfordern. Die Integration neuromorpher Elemente könnte dazu beitragen, den Energiebedarf eines Hybridsystems zu decken, wodurch Quantencomputeroperationen möglicherweise für breitere Anwendungen praktischer werden.
Umgang mit verrauschten Daten:
Beide Systeme können auf unterschiedliche Weise mit verrauschten oder unvollständigen Daten umgehen. Neuromorphes Computing kann lernen und sich an Datenrauschen anpassen, ähnlich wie biologische Neuronen funktionieren, während Quantencomputing nachweislich Rauschen in bestimmten Algorithmen ausnutzt, um einen Quantenvorteil zu erzielen. Diese gemeinsame Fähigkeit könnte in Hybridsystemen synergisiert werden, um die Robustheit gegenüber Fehlern oder Rauschen in realen Anwendungen zu verbessern.
Quantenneuromorphe Hardware:
Die Forschung hat das Konzept von Quantenmemristoren untersucht, die als Grundlage für Quantenneuronale Netzwerke dienen könnten. Diese Geräte könnten Quantenversionen neuromorpher Architekturen realisieren und möglicherweise die Rechenleistung beider Paradigmen verbessern, indem sie quantenverstärkte neuronale Verarbeitungs- oder Lernalgorithmen ermöglichen.
Skalierbarkeit und anwendungsspezifische Lösungen:
Neuromorphe Systeme können durch einfaches Hinzufügen weiterer Chips oder Neuronen skaliert werden, im Gegensatz zu Quantensystemen, bei denen die Skalierung derzeit aufgrund von Kohärenz und Fehlerraten mit erheblichen Herausforderungen verbunden ist. Zusammen könnten sie jedoch maßgeschneiderte Lösungen bieten, bei denen die Quantenverarbeitung für bestimmte rechenintensive Aufgaben innerhalb eines größeren neuromorphen Rahmens verwendet wird, der Daten und Lernen verwaltet, und so eine skalierbare Architektur für die Lösung komplexer Probleme schafft.
Maschinelles Lernen und KI-Beschleunigung:
Quantencomputing hat das Potenzial, Algorithmen des maschinellen Lernens zu beschleunigen, insbesondere in Bereichen wie quantengestütztes maschinelles Lernen oder Quantenreservoir-Computing. Neuromorphes Computing könnte hierzu beitragen, indem es eine Plattform für Echtzeit-Datenverarbeitung und Lernen am Rande der Algorithmen bietet, wobei Quantencomputing Rechenleistungssteigerungen für Trainings- oder Optimierungsphasen bietet.
Zusammenfassend lässt sich sagen, dass die Integration neuromorpher Technologien in das Quantencomputing den Weg für eine neue Klasse von Computersystemen ebnen könnte, die die Stärken beider Technologien nutzen, um Probleme effizienter und mit geringerem Energieverbrauch zu lösen und ein breiteres Anwendungsspektrum zu bieten, insbesondere in den Bereichen künstliche Intelligenz, Datenanalyse und Optimierung komplexer Systeme.https://x.com/QAN_Future/status/1869515238829162980?t=NqwsLXDIOf2Ky7DntVgrTw&s=19
buena suerte :-)
BOB Bank of Brainchip
The following is from Grok...
I asked how neuromorphic computing can complement quantum computing...
Neuromorphic computing and quantum computing are both emerging fields that aim to overcome the limitations of traditional computer architectures. Here are some reasons why they integrate well with each other:
Complementary computing paradigms:
Neuromorphic computing: Mimics the neural structure of the human brain, focusing on parallel processing, low power, and adaptive learning through pulsed neural networks. This paradigm is great for tasks that involve pattern recognition, learning from unstructured data, and real-time processing.
Quantum computing: Leverages quantum mechanical phenomena such as superposition and entanglement to perform computations that can solve certain problems exponentially faster than classical computers, particularly in areas such as optimization, cryptography, and quantum simulation.
Integration: Neuromorphic computing's strengths in dealing with complex, unstructured data and learning could complement quantum computing's ability to handle and optimize problems with huge solution spaces or where classical algorithms fail due to computational complexity. This could lead to hybrid systems where neuromorphic processing manages data input/output and learning, while quantum computing handles specific computational tasks such as optimization or simulation.
Energy efficiency:
Neuromorphic systems are designed to be more energy efficient compared to traditional computers. This is a significant advantage as quantum computers currently require extreme cooling to temperatures close to absolute zero. Integrating neuromorphic elements could help meet the energy needs of a hybrid system, potentially making quantum computing operations more practical for wider applications.
Dealing with noisy data:
Both systems can deal with noisy or incomplete data in different ways. Neuromorphic computing can learn and adapt to data noise, similar to how biological neurons function, while quantum computing has been shown to exploit noise in certain algorithms to achieve a quantum advantage. This shared ability could be synergized in hybrid systems to improve robustness to errors or noise in real-world applications.
Quantum neuromorphic hardware:
Research has explored the concept of quantum memristors, which could serve as the basis for quantum neural networks. These devices could realize quantum versions of neuromorphic architectures, potentially improving the computational power of both paradigms by enabling quantum-enhanced neural processing or learning algorithms.
Scalability and application-specific solutions:
Neuromorphic systems can be scaled by simply adding more chips or neurons, unlike quantum systems where scaling currently presents significant challenges due to coherence and error rates. Together, however, they could provide tailored solutions where quantum processing is used for specific computationally intensive tasks within a larger neuromorphic framework that manages data and learning, creating a scalable architecture for solving complex problems.
Machine learning and AI acceleration:
Quantum computing has the potential to accelerate machine learning algorithms, especially in areas such as quantum-assisted machine learning or quantum reservoir computing. Neuromorphic computing could contribute to this by providing a platform for real-time data processing and learning at the edge of the algorithms, with quantum computing offering computational power boosts for training or optimization phases.
In summary, integrating neuromorphic technologies with quantum computing could pave the way for a new class of computing systems that leverage the strengths of both technologies to solve problems more efficiently and with lower energy consumption, offering a wider range of applications, especially in the areas of artificial intelligence, data analytics, and optimization of complex systems.https://x.com/QAN_Future/status/1869515238829162980?
Last edited:
Dallas
Regular
Neuromorphic Computing: Future of Brain-Inspired AI Technology
Explore how Neuromorphic Computing is redefining AI with brain-inspired chips, ultra-low power consumption, and real-time learning for edge AI applications.
exelanceit.in
From Dallas's link above!
Picture a future where artificial intelligence seamlessly integrates into our daily lives, mimicking the efficiency and adaptability of the human brain. This isn’t science fiction—it’s the promise of neuromorphic computing, a revolutionary approach that’s set to transform the landscape of AI as we know it.
But why is this technology causing such a stir in the tech world? Traditional computing systems, despite their impressive capabilities, pale in comparison to the human brain’s energy efficiency and processing power. Enter neuromorphic computing—a game-changing solution that draws inspiration from the very organ it aims to emulate. By designing chips that function more like biological neural networks, researchers are unlocking new possibilities for AI that are faster, more efficient, and incredibly powerful.
As we delve into the world of neuromorphic computing, we’ll explore its fundamental principles, the groundbreaking chips making waves in edge AI, and spotlight innovative solutions like BrainChip’s Akida and TENN. Join us on this journey to uncover how brain-inspired technology is revolutionizing the future of AI and paving the way for smarter, more intuitive machines.
Traditional vs Neuromorphic Computing
Traditional Chips vs Neuromorphic Chips
Neuromorphic Computing: Revolutionizing the Future of AI with Brain-Inspired Technology
Exelance IT > Blogs > IT Industry > Neuromorphic Computing: Revolutionizing the Future of AI with Brain-Inspired Technology- ExeLance IT - Author
- December 19, 2024
- No Comments
Picture a future where artificial intelligence seamlessly integrates into our daily lives, mimicking the efficiency and adaptability of the human brain. This isn’t science fiction—it’s the promise of neuromorphic computing, a revolutionary approach that’s set to transform the landscape of AI as we know it.
But why is this technology causing such a stir in the tech world? Traditional computing systems, despite their impressive capabilities, pale in comparison to the human brain’s energy efficiency and processing power. Enter neuromorphic computing—a game-changing solution that draws inspiration from the very organ it aims to emulate. By designing chips that function more like biological neural networks, researchers are unlocking new possibilities for AI that are faster, more efficient, and incredibly powerful.
As we delve into the world of neuromorphic computing, we’ll explore its fundamental principles, the groundbreaking chips making waves in edge AI, and spotlight innovative solutions like BrainChip’s Akida and TENN. Join us on this journey to uncover how brain-inspired technology is revolutionizing the future of AI and paving the way for smarter, more intuitive machines.
Neuromorphic Approach to AI
Understanding Neuromorphic Computing
Neuromorphic computing is an innovative approach to artificial intelligence that mimics the brain’s neural networks. This field aims to create systems that potentially revolutionize AI and machine learning.Key Principles of Neuromorphic Computing
- Parallel Processing: Like the brain, neuromorphic systems process information in parallel, enabling faster and more efficient computations.
- Low Power Consumption: Designed to be energy-efficient, similar to the human brain’s remarkable efficiency.
- Adaptive Learning: Can learn and adapt in real-time, like biological neural networks.
| Feature | Traditional Computing | Neuromorphic Computing |
|---|---|---|
| Architecture | Sequential | Parallel |
| Power Efficiency | High consumption | Low consumption |
| Learning Ability | Predefined algorithms | Adaptive, real-time learning |
| Data Processing | Separate memory and processing | Integrated memory and processing |
Advantages of Neuromorphic AI
- Enhanced pattern recognition
- Improved natural language processing
- Real-time decision-making capabilities
- Reduced latency in edge computing
- More human-like AI interactions
Neuromorphic Chips and Edge AI
Revolutionizing Edge AI with Neuromorphic Chips
Neuromorphic chips are transforming edge AI, particularly with their advantages in power consumption and real-time processing.Key Benefits of Neuromorphic Chips
- Energy Efficiency
- Low Latency
- On-device Learning
- Adaptability
| Feature | Traditional Chips | Neuromorphic Chips |
|---|---|---|
| Power Consumption | High | Ultra-low |
| Processing Speed | Variable | Near real-time |
| Learning Capability | Limited | Continuous |
| Adaptability | Fixed | Dynamic |
Applications in Edge Devices
- Smart sensors for industrial IoT
- Autonomous vehicles
- Wearable health monitors
- Intelligent home assistants
BrainChip’s Akida and TENN
Akida: A Neuromorphic System-on-Chip
BrainChip’s Akida mimics the brain’s neural structure, offering ultra-low power consumption and real-time edge AI capabilities.Key Features of Akida
- Neural Network Processor for efficient processing
- On-chip learning without cloud connectivity
- Event-based processing for low latency
- Flexible architecture supporting CNNs and SNNs
TENN: Temporal Event Neural Network
TENN is BrainChip’s proprietary architecture designed for processing temporal data efficiently with improved accuracy.Applications of Akida and TENN
- Autonomous vehicles
- Industrial IoT
- Smart home devices
- Healthcare monitoring systems
- Cybersecurity
Conclusion
Neuromorphic computing is revolutionizing AI by mimicking the brain’s efficiency and adaptability. Technologies like BrainChip’s Akida and TENN are driving smarter, energy-efficient edge AI solutions that pave the way for faster, more intuitive, and intelligent systems in our daily lives.Good Afternoon Chippers ,
Very quiet on the volume front thus far , hour of power into the close ..... then late trades should be interesting.
CRANK IT. All good ... Time stamp 34.29
Also , on a side note , ASX trading hours days closed .
Best Wishes All .
Esq.
Very quiet on the volume front thus far , hour of power into the close ..... then late trades should be interesting.
CRANK IT. All good ... Time stamp 34.29
Also , on a side note , ASX trading hours days closed .
Trading calendars
2024
2025
>2024 Trading calendar
| PUBLIC HOLIDAY | DATES FOR 2024 | TRADING DAY[1] | SETTLEMENT ACTIVITY – ASX Settlement (CHESS) | SETTLEMENT ACTIVITY – ASX Clear (Derivatives) | BUSINESS DAY[2] |
| New Year’s Day | Monday 1 January | CLOSED | No Settlement | No Settlement | NO |
| Australia Day | Friday 26 January | CLOSED | No Settlement | No Settlement | NO |
| Good Friday | Friday 29 March | CLOSED | No Settlement | No Settlement | NO |
| Easter Monday | Monday 1 April | CLOSED | No Settlement | No Settlement | NO |
| ANZAC Day | Thursday 25 April | CLOSED | No Settlement | No Settlement | NO |
| King’s Birthday | Monday 10 June | CLOSED | No Settlement | No Settlement | NO |
| Last Business day before Christmas Day | Tuesday 24 December | CLOSE EARLY[3] | Settlement | Settlement | YES |
| Christmas Day | Wednesday 25 December | CLOSED | No Settlement | No Settlement | NO |
| Boxing Day | Thursday 26 December | CLOSED | No Settlement | No Settlement | NO |
| Last Business day of the Year | Tuesday 31 December | CLOSE EARLY[4] | Settlement | Settlement | YES |
Best Wishes All .
Esq.
Last edited:
Synsense have merged with Inivation, a rival DVS maker.PROPHESEE | Metavision Technologies
REVEAL THE INVISIBLE with the world's most advanced neuromorphic vision system, inspired by human vision and built on the foundation of neuromorphic engineering. PROPHESEE gives Metavision to machines, revealing what was previously invisible to them.
What are prophesee currently using that allows 2mW sensing power consumption?
Also good to see they have brainchip listed as a partner. Synsense who they have previously partnered with arent listed
https://www.synsense.ai/synsense-an...orm-leading-neuromorphic-technology-provider/
Not sure where you got the 2 mW figure, but does it relate to their newish GenX320 (~100k pixels) which has an ultra-low power mode "down to 36 microW".
https://www.prophesee.ai/event-based-sensor-genx320/
GENX320
KEY FEATURES
Resolution (px) 320×320
Ultra-low power mode (down to 36μW)
Optical format: 1/5”
Pixel latency @1k lux (μs) <150
Dynamic Range (dB) >120
Nominal contrast threshold (%) 25
Pixel size (μm) 6.3 x 6.3
Embedded features: Anti-flicker filtering (AFK) + Event-rate Controller (ERC) + Spatio-temporal Contrast filter (STC)
The nature of a DVS event sensor is that each pixel only produces an output signal when the change of light impinging on the pixel changes by more than a preset value.
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Fullmoonfever
Top 20
Surely not already 
Can't find details of what the real time accelerators are. Probs not us but....musing if they were an early NDA and been developing the "B" model which will go to market next year I think and if they had developed and tested and prepped for market then would need a licence obviously.
Could also explain the minimal up front licence fee and higher royalty if was a product ready for sale as royalties would be expected sooner
Stranger things have happened I guess haha
www.linkedin.com
Frontgrade Gaisler
4,543 followers
21h
Our latest GRMON4 debug tool offers enhanced control over SPARC LEON and RISC-V NOEL processors. With advanced features such as advanced breakpoints, GUI, and support for JTAG, Ethernet, and SpaceWire debug connections, it provides a comprehensive debugging experience. A notable addition is the capability to debug all three processing elements in the new GR716B microcontroller, including the LEON3FT main processor and the two embedded Real Time Accelerators.
Congratulations to Arvid Björkengren and Anton Bäckström for the release of this brand new product! #spacecomputing #grmon4 #sparc #riscv #noelv #leon3ft Explore these new features on our updated website: [https://lnkd.in/dwuH3Pzn)
www.gaisler.com
Real Time Accelerators
The GR716B includes two real-time accelerators (RTA), whose function is off-loading the main LEON3 processor of demanding real-time control tasks. Each RTA has access to tightly coupled memories for instruction and data, thereby allowing single-cycle access and facilitating the design of realtime application software. This also enables control of multiple real-time critical applications from one and the same RTA.
Can't find details of what the real time accelerators are. Probs not us but....musing if they were an early NDA and been developing the "B" model which will go to market next year I think and if they had developed and tested and prepped for market then would need a licence obviously.
Could also explain the minimal up front licence fee and higher royalty if was a product ready for sale as royalties would be expected sooner
Stranger things have happened I guess haha
Frontgrade Gaisler on LinkedIn: #spacecomputing #grmon4 #sparc #riscv #noelv #leon3ft
Our latest GRMON4 debug tool offers enhanced control over SPARC LEON and RISC-V NOEL processors. With advanced features such as advanced breakpoints, GUI, and…
Frontgrade Gaisler
4,543 followers
21h
Our latest GRMON4 debug tool offers enhanced control over SPARC LEON and RISC-V NOEL processors. With advanced features such as advanced breakpoints, GUI, and support for JTAG, Ethernet, and SpaceWire debug connections, it provides a comprehensive debugging experience. A notable addition is the capability to debug all three processing elements in the new GR716B microcontroller, including the LEON3FT main processor and the two embedded Real Time Accelerators.
Congratulations to Arvid Björkengren and Anton Bäckström for the release of this brand new product! #spacecomputing #grmon4 #sparc #riscv #noelv #leon3ft Explore these new features on our updated website: [https://lnkd.in/dwuH3Pzn)
GR716B | Gaisler
The GR716B is a radiation-hardened microcontroller featuring the fault-tolerant LEON3FT SPARC V8 processor and two Real-Time Accelerators.
www.gaisler.com
Real Time Accelerators
The GR716B includes two real-time accelerators (RTA), whose function is off-loading the main LEON3 processor of demanding real-time control tasks. Each RTA has access to tightly coupled memories for instruction and data, thereby allowing single-cycle access and facilitating the design of realtime application software. This also enables control of multiple real-time critical applications from one and the same RTA.
Hi FMF,Surely not already
Can't find details of what the real time accelerators are. Probs not us but....musing if they were an early NDA and been developing the "B" model which will go to market next year I think and if they had developed and tested and prepped for market then would need a licence obviously.
Could also explain the minimal up front licence fee and higher royalty if was a product ready for sale as royalties would be expected sooner
Stranger things have happened I guess haha
Frontgrade Gaisler on LinkedIn: #spacecomputing #grmon4 #sparc #riscv #noelv #leon3ft
Our latest GRMON4 debug tool offers enhanced control over SPARC LEON and RISC-V NOEL processors. With advanced features such as advanced breakpoints, GUI, and…
Frontgrade Gaisler
4,543 followers
21h
Our latest GRMON4 debug tool offers enhanced control over SPARC LEON and RISC-V NOEL processors. With advanced features such as advanced breakpoints, GUI, and support for JTAG, Ethernet, and SpaceWire debug connections, it provides a comprehensive debugging experience. A notable addition is the capability to debug all three processing elements in the new GR716B microcontroller, including the LEON3FT main processor and the two embedded Real Time Accelerators.
Congratulations to Arvid Björkengren and Anton Bäckström for the release of this brand new product! #spacecomputing #grmon4 #sparc #riscv #noelv #leon3ft Explore these new features on our updated website: [https://lnkd.in/dwuH3Pzn)
GR716B | Gaisler
The GR716B is a radiation-hardened microcontroller featuring the fault-tolerant LEON3FT SPARC V8 processor and two Real-Time Accelerators.
Real Time Accelerators
The GR716B includes two real-time accelerators (RTA), whose function is off-loading the main LEON3 processor of demanding real-time control tasks. Each RTA has access to tightly coupled memories for instruction and data, thereby allowing single-cycle access and facilitating the design of realtime application software. This also enables control of multiple real-time critical applications from one and the same RTA.
"Real time control tasks" and "instructions and data" sounds more like a program-controlled processor.
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