BRN Discussion Ongoing

[manny100]

I know this is not SNN, but a nice visualisation of CNN

Its just as well AKIDA converts CNN to SNN.

 

[Frangipani]

MB signed an MOU with Ontario in 2022 for cooperation across the electric vehicle chain. In addition to the Wellington Uni research centre linked to Eliasmith, MB has joined the Ontario start-up incubator OVIN.

Nope, and neither is it Duke University…
The name of the Canadian city (and hence that of this uni) ain’t about who defeated Napoleon, but where it happened.

Almost 159 years later, a catchy tune with a reference to that battle ground secured a Swedish pop quartet a different kind of victory, with music being their only (but extremely effective) weapon:

 

[Diogenese]

Monica Politano from ESA works with AI:

https://www.linkedin.com/in/monica-politano-248b50/recent-activity/all/

Just like Alica, Monica led me to a magical rabbit hole:

https://esastar-publication-ext.sso.esa.int/ESATenderActions/details/75581

ONBOARD DATA HANDLING SUB-SYSTEM FOR AUTONOMOUS SATELLITES (ARTES AT 4G.044)

Tender Action Number: 1-12227 — Activity Number: 1000039882

Clarification Request Deadline 01/11/2024 13:00 CET1

Closing Date Extension Request Deadline 01/11/2024 13:00 CET1

Announcement Date 08/03/2024

Last Update On 18/11/2024 14:25 CET1

Update Reason Status Changed

The objective is to develop an onboard data handling architecture capable of autonomously acquiring, processing and interpreting housekeeping and telemetry data and taking the required actions without ground intervention. The activity will develop a data handling sub-system demonstrator to validate the intelligent functionalities, including prognostic and health management (PHM) capabilities

Targeted Improvements: Enabling technology development for future fully autonomous satellites which are capable to operate independently, without the need of immediate operator intervention.

Description: Current satellite operations are centred around human interactions from ground, which results in considerable inefficiencies. Limited windows of communication with the satellite, latency, and resource-limited ground infrastructure are the main reasons for the inefficiencies, which become more prominent for large constellations of satellites. This activity reduces the dependence of the spacecraft on ground stations by developing an intelligent onboard data handling architecture, using Artificial Intelligence (AI) to autonomously process, interpret housekeeping and telemetry data and take decisions on needed actions in real-time. This is considered an essential capability for management of future satellite constellations. The current data handling architectures and core building blocks such as the onboard computer (OBC) and Remote Terminal Unit (RTU) need to be redesigned to allow autonomous telemetry acquisition for all spacecraft units (incl. telemetry format standardisation), data fusion of big data sets and processing with selected AI-based algorithms, derivation of performance profiles, identification of anomalous behaviour and failure prediction. Data acquisition, either directly by the OBC or by the RTU, will entail the development of a block capable to acquire, convert and calibrate all onboard telemetries at configurable sampling rates.

Data fusion and processing will be facilitated by using AI and Machine Learning (ML), enabling the OBC to analyse large data sets and autonomously take the required actions. This includes detection of potentially hazardous situations, such as solar flares or collisions withspace debris, making intelligent decisions and taking appropriate actions to mitigate or avoid these situations.

In addition to the development of the intelligent data handling architecture, the activity shall also provide the onboard computer (OBC) with the capability to execute an embedded version of a system digital twin. This digital twin implementation will enable the OBC to simulate and mirror the behaviour of the actual satellite system in real-time, allowing for enhanced monitoring, diagnostics, and performance optimisation. By incorporating a system digital twin into the OBC, it will be possible to achieve a higher level of autonomy and proactive decision-making within the satellite operations.

This activity will develop a data handling sub-system demonstrator which will include breadboards of the OBC and RTU with AI/ML-based algorithms, to validate the developed intelligent functionalities, including Prognostic and Health Management (PHM) capabilities.

Directorate Dir. of Connectivity Secure Comm. Estabilishment ESTEC

Open Date 28/06/2024 09:41 CET0 Closing Date 15/11/2024 13:00 CET1

ECOS Required No Classified No

Price Range > 500 KEURO

Authorised Contact Person Florence Glandieres Initiating Service CSC-STS

IP Measure N/A

Prog. Reference E/0534-01I - CC - Adv Tech 4.0.2

Tender Type Open Competition

Open To Tenderers From

AT+BE+CA+CH+CZ+DE+DK+GR+ES+FI+FR+GB+HU+IE+IT+LU+NL+NO+PL+PT+RO+SE

Technology Keywords 1-D-I-Machine Learning for On-board Data Subsystems

Products Keywords 2-F-1-a-Central Data Management Units (CDMU) or Satellite Management Units (SMU) 2-F-1-f-Remote Terminal Units


Applicants needed to be European, so FG or EdgX could have submitted an Akida-based bid.

 

[Frangipani]

Non BRN, Beemotion.ai (NVISO)

Cycliq teams up with BeEmotion to integrate vehicle detection technology into bike cameras - The Sentiment

Cycliq (ASX: CYQ) has entered into a 10-year licence agreement with BeEmotion, formerly NVISO Group, to integrate AI technology into its Fly cameras and edge devices. Cycliq is an Australian company that manufactures innovative cycling safety products, including the Fly6 and Fly12 cameras, with...

thesentiment-com-au.cdn.ampproject.org

"The integration of BeEmotion’s AI technology will be applied to both new and existing Cycliq products, including improvements in real-time vehicle detection, object recognition, and edge-computation capabilities(hey that's us!? ) This strategic move is expected to advance Cycliq’s technology and provide enhanced safety features for cyclists"

Looks like it's early days though..

So we know, as has been posted by @Frangipani from memory, that BeEmotion was just at the EdgeTech+ expo in Japan.

They were showcasing their smart mobility (in cabin) using Akida.

Was just watching a recent, well 5 mths old, video with The Market Bull and the CEO of BeEmotion.

They're focusing on smart mobility, smart health and smart living.

She mentions what we know with their algo work with Panasonic and Paincheck.

What I did like is that she said they are currently working with some luxury car brands (1.27). Wonder if that work is just their algo side or they are also highlighting Akida too like at the expo.




Off that back of this I see they signed a 10 year licence agreement with Cycliq (ASX CYQ) in late Aug this year.

HERE

Liked that they are also planning to deploy on "new" software and hardware. Again, hope we may be in the discussion somewhere.

Cycliq to licence AI solution from BeEmotion
HIGHLIGHTS
• 10-year license agreement with NVISO Group Ltd (to be renamed
BeEmotion.ai Ltd) to integrate AI technology into its range of Fly cameras and
edge devices
• Cycliq planning to deploy the BeEmotion technology in a range of new and
existing software and hardware products
• A$352,000 capital raise envisaged

Snip from the Ann I also liked and wonder if this will extend to real time processing with ultra low power etc too at some point.

View attachment 73910

Check this out!
Adam Osseiran is now a member of the Advisory Boards of both BeEmotion.AI (formerly known as NVISO) and Cycliq!

Dot. Dot. Dot. Dot…


“Adam’s knowledge of Neuromorphic Computing and the Hardware components of Edge AI will help Cycliq in the design of vision applications both in-camera and via control box modules that can elevate the features of our current products and expand use cases across and beyond cycling.”

https://data-api.marketindex.com.au/api/v1/announcements/XASX:CYQ:6A1244478/pdf/inline/advisory-board-appointment

 

[Dallas]

Sign Up | LinkedIn

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

www.linkedin.com

 

[Frangipani]

Neuromorphic Computing: Revolutionizing the Future of AI with Brain-Inspired Technology | ExeLance IT

🌟 Discover how neuromorphic computing is revolutionizing the future of AI by mimicking the brain's efficiency and adaptability. Learn about groundbreaking technologies like BrainChip's Akida and TENN that are shaping smarter, energy-efficient edge AI solutions. Let’s explore the potential of...

www.linkedin.com

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

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.

Traditional vs Neuromorphic Computing

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

Traditional Chips vs Neuromorphic Chips

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.

---

While this is probably just an AI-generated text, the company behind this blog and website is a real-life IT solutions provider from India…

About Us | Pioneering IT Services for Business Transformation

Discover ExeLance IT, an innovative IT company offering IT services that transform businesses worldwide. Learn about our vision and team.

exelanceit.in

 

[MDhere]

Sign Up | LinkedIn

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

www.linkedin.com

I know very few words in German. A boy on a train in Switzerland once said to me Iche leibe diche I was like ummm this is awkward lol I'm gathering your post Dallas is as promising as the boy on the train if I had fully understood him at the time lol

 

[FiveBucks]

Sony is working with AMD to integrate CNN AI learning into future games/consoles.


What's wrong with SNN Sony???

https://videocardz.com/newz/sony-unveils-project-amethyst-next-gen-ai-enhanced-hardware-in-collaboration-with-amd

 

[JB49]

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]

There will 100% NOT be another IP announcement, this Friday morning.

 

[Tothemoon24]

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]

It’s going to be one big celebration when our friends from Tata put pen to paper , cmon Santa

 

[Tothemoon24]

This is pretty cool from Valeo ,
projecting images onto the road
I recall Sean mentioning lighting

 

[Tuliptrader]

It’s going to be one big celebration when our friends from Tata put pen to paper , cmon Santa



View attachment 74481


View attachment 74482

Could this possibly be somewhere that Akida TENN's could shine?

TT

 

[IloveLamp]

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…

www.linkedin.com

 

[HopalongPetrovski]

There will 100% NOT be another IP announcement, this Friday morning.

 

[Dallas]

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

 

[Dallas]

x.com

x.com

 

[Boab]

IYK,YK

 

[buena suerte :-)]

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

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?