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Leuven, Belgium
251-500 Employees
The TEMPO project is based on collaborative research between world-leading research centres, large and small industrial enterprises, and universities. AI4DI’s (Artificial Intelligence for Digitizing Industry) mission is bringing AI from the cloud to the edge and making Europe a leader in silicon-born AI by advancing Moore’s law and accelerating edge processing adoption in different industries through reference demonstrators. The ANDANTE (AI for New Devices And Technologies at the Edge) project promotes innovative hardware and software deep-learning solutions for future IoT at the edge products that combine extreme power efficiency as well as robust and powerful cognitive computing capabilities. EBRAINS is a new digital research infrastructure, created by the EU-funded Human Brain Project, that gathers an extensive range of data and tools for brain-related research. TEMPO (Technologies and hardware for neuromorphic computing) is a collaborative research project. The project activities and results are reported in the project deliverables. It is a conference & exhibition dedicated to semiconductors and their massive potential as a key driver for deep-tech innovations to help solve major challenges in industries and society worldwide. A magnificent consortium paved the way towards embedded AI by developing innovative neuromorphic technologies.
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Homepage - TEMPO
... Low-power chips for AI applicationsBased on neuromorphic hardware and technologiesAbout TempoTEMPO (Technologies and hardware for neuromorphic computing) is ...
Geneva, Switzerland
2013
The EBRAINS Data and Knowledge Services increase the efficiency and productivity in research by making data discoverable and reusable. Brain atlases provide spatial reference systems for neuroscience that allow navigation, characterisation and analysis of information based on anatomical location. The service for embodied simulation developed by the Human Brain Project, now offered by EBRAINS.
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SGA2 Phase
... SP9 Neuromorphic Computing ...
Shufune, Ethiopia
1-10 Employees
2020
By subscribing you agree to our Privacy Policy. Made with by Intech Analytica - Privacy Policy.
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Home – Intech Analytica The all-in-one Tech Blog. Analysis, Visualization, & Prediction of Data.
... Is Neuromorphic Computing The Answer For Autonomous Driving And Personal Robotics? ...
Valencia, Spain
11-50 Employees
2019
iPronics aims to expand photonics processing to all the layers of the industry. This game-changing technology has a wide range of applications, including 5/6 G communications, intelligent transceivers and switches, tensor cores for neuromorphic computing, lidar and aerospace surveillance and communications. The total non-recoverable engineering costs for developing a single iteration of an application specific photonic integrated circuit (ASPIC) design are around €550k-€1,700k, and the development time is currently 12 months. As the original ASPIC design is rarely error-free, typically 2-3 iterations are required, taking the cumulative effort to 2-3 years and the cost into the region of €940k-€4.6m. iPronics cuts the typical development time of an ASPIC by 90% and the associated costs by 95%.
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About - iPronics Programmable Photonics
... wide range of applications, including 5/6 G communications, intelligent transceivers and switches, tensor cores for neuromorphic computing, lidar and aerospace surveillance and communications The total non-recoverable engineering costs for developing a single iteration of an application […] ...
Boonton, United States
51-100 Employees
2016
We deliver market research reports on time everytime. We are always dedicated to turn decisions into actions. We deliver best in-class market research reports in addition to consultancy services. We are known for custom research studies on diverse markets. We diffuse all our services for enabling a hassle-free and centralized market research solution for all kinds of needs. With a team of 500+ Analysts and subject matter experts, VMR leverages internationally recognized research methodologies for data collection and analysis, covering over 15,000 high impact and niche markets. This robust team ensures data integrity and offers insights that are both informative and actionable, tailored to the strategic needs of businesses across various industries. Verified Market Research® is also a member of ESOMAR, an organization renowned for setting the benchmark in ethical and professional standards in market research.
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Global Neuromorphic Computing, AI Hardware And Edge Analytic Market Size By Offering (Hardware, Software), By Application (Image Recognition, Signal Recognition), By Geographic Scope And Forecast
... Global Neuromorphic Computing, AI Hardware And Edge Analytic Market Size By Offering (Hardware, Software), By Application (Image Recognition, Signal Recognition), By Geographic Scope And ...
Newport, United States
11-50 Employees
2017
Entanglement, Inc. is an early-stage deep technology company dedicated, inter alia, to providing unprecedented commercial access to diverse and advanced computing systems (including quantum computing, high-performance / super- computing and purpose-built computing systems) to a broad range of customers. Focused to accelerate the development of quantum information science (QIS) and artificial intelligence (AI) without enormous up-front capital investment, Entanglement has designed an environment for rapid experimentation and breakthroughs. Through the democratization of these integrated computing capabilities and the experience of its team, the company lowers the barriers to entry for solving seemingly unsolvable real-world problems today. It provides a bridge from classical to quantum computation. The company is privately held and was founded in 2017.
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MULTIPLE QUANTUM COMPUTERS SUPERCOMPUTING NEUROMORPHIC COMPUTING AI ACCELERATOR CLUSTERS
... MULTIPLE QUANTUM COMPUTERS SUPERCOMPUTING NEUROMORPHIC COMPUTING AI ACCELERATOR ...
United States
101-250 Employees
1999
Sie können die gesamte Seite Intel.com mühelos auf verschiedene Weisen durchsuchen. Sie können auch die Quick-Links unten versuchen, um sich Ergebnis der beliebtesten Suchvorgänge anzusehen. Intel hat deshalb die erste Plattform für Echtzeit-Deepfake-Erkennung entwickelt. Diese innovative KI-Lösung mit FakeCatcher und Intel® Xeon® Prozessoren kann ohne Verzögerung und mit 96%iger Genauigkeit echte Menschen von Fakes unterscheiden. Mit mehr Leistung, KI-Kernen und Speicherbandbreite vereint die Intel® Arc™ Pro A60 Grafik modernste Bildverarbeitungstechnik und umfangreiche Möglichkeiten der Content-Gestaltung für professionelle Anwender im Standardformat für einen einzelnen Steckplatz.
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How neuromorphic computing will change our world in wonderful ways
... Learn how neuromorphic computing will change our world in wonderful ways. ...
Potsdam, Germany
1-10 Employees
Mabkoda – Hand in Hand von der Idee zum Objekt. Auch Umbauarbeiten und Sanierungen zählen zu unserem Leistungsportfolio.
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mabkoda Bau und Umwelttechnik GmbH
... European Ins. for Neuromorphic Computing in Heidelberg – ...
Montpellier, France
11-50 Employees
2020
NeurONN is a research project funded by H2020 EU’s research and innovation programme with core subject “Energy-efficient bio-inspired devices accelerate route to brain-like computing”. The EU-funded NeurONN project will showcase a novel and alternative neuromorphic computing paradigm based on energy-efficient devices and architectures. Furthermore, NeurONN has initiated an Industrial Advisory Board which consists of members from Intel Corporation and Prophesee. Centre National de la Recherche Scientifique (CNRS) is the Coordinator of the Project. CNRS is a public-funded research organization, under the administrative authority of France’s Ministry of Research and the largest fundamental research organization in Europe. IBM Research – Zurich is the European branch of the IBM Research Division. Silvaco is a leading EDA provider of software tools used for procee and device development and for analog/mixed-signal, power IC and memory design.
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NeurONN – Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing
... NeurONN – Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing ...
Technologies which have been searched by others and may be interesting for you:
Some interesting numbers and facts about the results you have just received for Neuromorphic Computing
| Country with most fitting companies | United States |
| Amount of fitting manufacturers | 19 |
| Amount of suitable service providers | 10 |
| Average amount of employees | 51-100 |
| Oldest suiting company | 1999 |
| Youngest suiting company | 2020 |
Neuromorphic computing refers to a subset of computing technology designed to emulate the neural structure and operation of the human brain. This approach to computing employs specialized hardware, often integrating systems such as silicon-based transistors and memristors, to mimic the brain's network of neurons and synapses. The fundamental architecture diverges from traditional computing paradigms by focusing on parallel processing and a high degree of interconnectivity, enabling the efficient processing of complex, nonlinear problems akin to natural neural processing. Neuromorphic systems excel in areas requiring pattern recognition, sensory data processing, and decision-making tasks, offering significant advantages in terms of speed and energy efficiency compared to conventional computers. The impact of neuromorphic computing within its field is profound, promising transformative advancements in artificial intelligence, robotics, and the Internet of Things (IoT). By closely replicating the mechanisms of human cognition, neuromorphic computing not only enhances machine learning algorithms and data analysis techniques but also paves the way for more intuitive human-computer interactions and adaptive, intelligent systems. This paradigm shift towards brain-inspired computing holds the potential to revolutionize a myriad of sectors, from healthcare diagnostics and autonomous vehicles to environmental monitoring and beyond, by offering smarter, more efficient technological solutions.
1. Energy Efficiency:
One of the standout advantages of neuromorphic computing is its exceptional energy efficiency. Mimicking the human brain's architecture allows these systems to process information more efficiently than traditional computing methods, significantly reducing power consumption. This feature is especially beneficial for powering small, portable devices and large-scale applications requiring minimal energy use.
2. Real-time Processing:
Neuromorphic computing excels in handling data in real-time, thanks to its ability to process information in parallel. This contrasts with conventional computing that processes tasks sequentially, making neuromorphic systems particularly adept at tasks requiring instant analysis and response, such as autonomous vehicle navigation or real-time data analytics.
3. Adaptability:
Another key benefit is the adaptability of neuromorphic systems. They are designed to learn and adapt from the information they process, similar to how the human brain learns and evolves. This feature enables them to handle new, unanticipated scenarios without needing explicit reprogramming, making them ideal for applications in dynamic environments.
4. Handling Complexity:
Neuromorphic computing is uniquely capable of managing complex patterns and data. Its brain-inspired architecture allows it to recognize patterns and make decisions based on ambiguous or incomplete information, a challenging task for traditional computing systems. This makes it particularly useful for fields like artificial intelligence, where understanding complex patterns is crucial.
While evaluating the different suppliers make sure to check the following criteria:
1. Technological Expertise and Innovation
Assess the supplier's track record in neuromorphic computing, including patents, research publications, and contributions to the field. Their ability to innovate and adapt to new technologies is crucial.
2. Product Scalability
Evaluate the scalability of their solutions. Can they accommodate the growth of your operations or the increasing complexity of neural network models?
3. Energy Efficiency
Consider the energy efficiency of their hardware. Neuromorphic computing devices should consume significantly less power than traditional computing systems.
4. Customization Capabilities
Determine their ability to tailor solutions to fit specific needs. Customization is often key in neuromorphic computing applications.
5. Support and Maintenance Services
Look into the level of support and maintenance offered. Continuous technical support and clear maintenance plans are essential for long-term operational stability.
6. Security Features
Security features should not be overlooked. Ensure the supplier's products include robust security measures to protect sensitive data.
7. Cost-effectiveness
While not compromising on quality and performance, consider the cost-effectiveness of their solutions. Compare pricing models and the total cost of ownership.
8. Industry Reputation and Client Feedback
Research their reputation within the industry and feedback from current or past clients. This can provide insights into the reliability and performance of their solutions.
Neuromorphic computing, mimicking the human brain's neural structure and operation, is revolutionizing business-to-business (B2B) applications across various industries. In the financial sector, neuromorphic computing enables advanced fraud detection systems. By simulating the human brain's pattern recognition capabilities, these systems can analyze transaction patterns in real-time, identifying fraudulent activities with unprecedented accuracy and speed, thereby saving millions in potential losses. In the realm of manufacturing, neuromorphic computing facilitates predictive maintenance. By continuously learning from data generated by machinery, such as vibration, temperature, and sound, neuromorphic systems can predict equipment failures before they occur. This capability significantly reduces downtime and maintenance costs, enhancing operational efficiency and productivity. The technology also plays a pivotal role in the pharmaceutical industry by accelerating drug discovery. Neuromorphic computing can process and analyze vast datasets of chemical compounds and biological data much faster than traditional computing methods. This speed enables the identification of potential drug candidates at an accelerated pace, reducing the time and cost associated with bringing new drugs to market. In the automotive sector, neuromorphic computing is integral to the development of autonomous vehicles. By processing information from various sensors in real-time, neuromorphic systems allow vehicles to make instantaneous decisions, closely mimicking human reflexes. This capability is crucial for the safety and reliability of self-driving cars, paving the way for their commercial viability. These use cases across different industries underscore neuromorphic computing's transformative potential in B2B applications, heralding a new era of efficiency, innovation, and problem-solving capabilities.
As of my last update in 2023, neuromorphic computing primarily hovers between TRL 3 and TRL 4. This classification stems from the fact that researchers have successfully demonstrated the basic principles of neuromorphic computing in a laboratory environment (TRL 3), and small-scale integration into systems for initial validation has begun (TRL 4). The technical reasons anchoring neuromorphic computing at these levels include the ongoing development of hardware that mimics the neural structures and functioning of the human brain, such as silicon-based neurons and synapses, which are critical for achieving the parallel processing capabilities and energy efficiency envisioned for this technology. However, challenges in materials science and fabrication techniques are significant hurdles. The complexity of replicating biological neural networks' efficiency and adaptability in hardware form necessitates advancements in understanding brain functionalities and translating these into electronic equivalents. Furthermore, the development of software and algorithms capable of leveraging neuromorphic hardware's unique architecture is in its nascent stages, requiring further research and innovation. These technical challenges underscore the reason why neuromorphic computing is yet to move beyond the early stages of system integration and validation in real-world environments, encapsulating its position at the lower rungs of the Technology Readiness Level scale.
In the short-term, neuromorphic computing is poised for significant advancements in energy efficiency and miniaturization. Developers are focusing on creating more sophisticated synaptic devices and neurons that mimic the human brain's efficiency. This will lead to the production of neuromorphic chips capable of performing complex cognitive tasks such as pattern recognition and decision making with substantially lower power consumption compared to traditional computing systems. These enhancements will be particularly beneficial for mobile and edge computing devices, where power efficiency is critical. Mid-term developments are expected to usher in the integration of neuromorphic computing with artificial intelligence (AI) and machine learning algorithms. This phase will see the creation of systems that can learn and adapt in real-time, without the need for reprogramming. The convergence of neuromorphic computing and AI technologies will enhance the capabilities of autonomous systems, including drones and self-driving vehicles, by enabling them to make more nuanced decisions in dynamic environments. Additionally, this period will witness the expansion of neuromorphic computing into new application areas such as personalized medicine, where it can simulate complex biological processes. Looking into the long-term, the ambition is to achieve fully autonomous neuromorphic systems that can interact with the physical world in a manner akin to human cognition. These systems will be capable of understanding and interpreting sensory inputs at a highly advanced level, facilitating breakthroughs in robotics, prosthetics, and interactive computing. Furthermore, the development of global neuromorphic networks, akin to a decentralized brain, could revolutionize the internet, making it more intuitive and responsive to human needs. This era will not only redefine computational paradigms but also blur the lines between biological and artificial intelligence, leading to profound implications for society and technology.
