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Neuronspike
Boston, United States
B
1-10 Employees
-
Key takeaway
Neuronspike Technologies is focused on developing hardware-aware Artificial General Intelligence (AGI) through brain-inspired AI models and chipsets. Their innovative approach enables the deployment of powerful AI models at edge devices, potentially enhancing computational efficiency and performance.
Reference
Core business
Unified AI-as-a-service platform - NeuronSpike | New York
Neuronspike is technology company developing hardware-aware general artificial intelligence platform with the brain-inspired knowledge engines.
NimbleAI.eu
Arrasate / Mondragón, Spain
A
51-100 Employees
-
Key takeaway
NimbleAI is developing a neuromorphic sensing and processing 3D integrated chip that mimics the energy-efficient light sensing of eyes and the visual information processing of brains. This innovative approach aims to deliver significant performance and efficiency improvements over traditional CPU/GPUs used for frame-based video processing.
Reference
Core business
Nimble AI - We design a neuromorphic sensing & processing 3D integrated chip - NimbleAI
We are designing a neuromorphic sensing & processing 3D integrated chip inspired by the detection of light in eyes and the processing of visual information in brains Learn more What makes us different CPU/GPUs are very inefficient in comparison to eyes and brains, which are honed by natural selection. NimbleAI leverages the key principles of ... Read more
GrAI Matter Labs
Paris, France
A
51-100 Employees
2016
Key takeaway
GrAI Matter Labs specializes in neuromorphic computing through its brain-inspired chips that mimic human behavior, highlighting their innovative NeuronFlow™ technology. Their edge AI processor offers low-power, ultra-low latency processing, making it ideal for applications in robotics, AR/VR, and drones.
Reference
Core business
GrAI Matter Labs | Fastest Edge AI Processor
GrAI Matter Labs has created the fastest edge AI processor for machine vision in robotics, AR/VR, drones and more. It achieves ultra-low latency at low power by leveraging sparsity.
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BrainChip
Aliso Viejo, United States
B
11-50 Employees
2006
Key takeaway
BrainChip is a leader in edge AI on-chip processing, focusing on making devices AI-smart through innovative technologies like event-based processing and on-chip learning. Their approach brings common sense to sensor data processing, enabling machines to make inferences more efficiently.
Reference
Product
Technology - BrainChip
Explore all the incredible technology Brainchip has to offer!
EU H2020 NEURONN
Montpellier, France
A
11-50 Employees
2020
Key takeaway
NeurONN is a research project that focuses on developing energy-efficient, bio-inspired devices to advance neuromorphic computing. The project aims to demonstrate a novel computing paradigm that mimics brain-like processing, highlighting its relevance to the field.
Reference
Core business
NeurONN – Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing
NeurONN 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 project with duration of 42 months (1 January 2020 – 30 June 2023) brings together leading European research
Axelera AI
Eindhoven, Netherlands
A
11-50 Employees
2021
Key takeaway
The company, Axelera AI, is focused on democratizing Artificial Intelligence through innovative technologies like digital in-memory computing and neural network optimization. Their solutions enhance AI at the Edge, offering high efficiency and scalability for deploying machine learning models.
Reference
Product
Technology - Axelera AI
AI at the Edge requires more than a single accelerator IC. That’s why Axelera AI offers scalable solutions that can be easily integrated into any system.
NeuReality
Hof HaCarmel Regional Council, Israel
B
1-10 Employees
2018
Key takeaway
NeuReality is dedicated to making AI adoption easy and accessible, which aligns with the growing interest in neuromorphic computing as a means to enhance AI capabilities. Their innovative, AI-centric solutions focus on optimizing and scaling AI workflows, potentially offering new approaches relevant to the field of neuromorphic computing.
Reference
Core business
NeuReality | We Make AI Easy
NeuReality is the first complete, system-level solution designed to address the challenges of optimizing, deploying, managing, and scaling AI workflows.
NovuMind
United States
B
11-50 Employees
2015
Key takeaway
NovuMind is at the forefront of neuromorphic computing with its NovuTensor chip, which performs tensor computation efficiently, addressing the high computational power demands of AI inference applications. Their innovative hardware architecture and domain-specific design enable real-time deployment of neural network models, making advanced AI capabilities more accessible and practical for various industries.
Reference
Core business
Company - Novumind
NovuMind helps companies put the power of AI in their products and services. Our NovuTensor chip is an industry first, performing tensor computation at the speed of silicon and providing breakthrough performance-to-power ratios. Our full stack of deep learning tools makes it easy to use NovuTensor in a range of applications, from embedded to cloud. AI will transform many industries, from healthcare to transportation. But there have been barriers to widespread adoption. One has been the computational power demanded by deep learning deployment. Once a neural network model has been trained, it is deployed in an end-user application where it provides “inference” intelligence, such as real-time object detection in video. Running the deployed model in real-time is computationally intensive. This becomes a stumbling block if the app must run in a device with a limited power budget. An alternative may be to rely on computational acceleration in the cloud, but scaling that to support large numbers of users may also be a challenge. Power and cooling requirements can make it prohibitive to operate inference acceleration at large scale in the data center, with traditional CPU/GPU-based approaches. Another barrier has been the lack of AI expertise. Few companies have the luxury of an in-house…More DetailsCompany
Artificial Learning
London, United Kingdom
A
1-10 Employees
2012
Key takeaway
Artificial Learning Ltd is developing neuromorphic computing solutions by integrating advanced machine learning algorithms, such as Restricted Boltzmann Machines and Deep Belief Networks, into application-specific integrated circuits (ASICs).
Reference
Product
Machine Learning on a Chip | Artificial Learning
NEUROLOGIQ
Siegen, Germany
A
11-50 Employees
2018
Key takeaway
NEUROLOGIQ is focused on developing AI solutions for the mid-sized industry, addressing the specific challenges faced by SMEs and providing practical applications that could relate to advancements in neuromorphic computing.
Reference
Service
Engineering ● NEUROLOGIQ ● The AI makers
Technologies which have been searched by others and may be interesting for you:
Neuromorphic computing refers to a design approach that mimics the neural structures and functioning of the human brain to process information. By using artificial neurons and synapses, it enables more efficient computation, particularly for tasks involving sensory processing and pattern recognition. This technology seeks to enhance machine learning and AI capabilities, allowing systems to respond to stimuli in real-time while consuming significantly less power compared to traditional computing architectures. The architecture typically includes hardware designed to emulate brain-like functions, which can lead to advancements in cognitive computing and robotics. As a result, neuromorphic systems are poised to revolutionize how machines learn and adapt, pushing the boundaries of artificial intelligence.
Neuromorphic computing mimics the neural structure and functioning of the human brain to process information. It utilizes specialized hardware that incorporates artificial neurons and synapses, enabling it to perform tasks such as pattern recognition and sensory processing with high efficiency. By structuring computations similarly to biological neural networks, neuromorphic systems can handle tasks in parallel, leading to reduced energy consumption compared to traditional computing architectures. This approach allows for adaptive learning, where the system can modify its connections based on experience, thereby optimizing performance over time.
1. Energy Efficiency
Neuromorphic computing mimics the neural architecture of the human brain, leading to significant energy savings. This technology processes information in a manner that reduces the power consumption compared to traditional computing systems, making it ideal for mobile and embedded applications.
2. Enhanced Processing Speed
By utilizing parallel processing and event-driven architectures, neuromorphic systems can perform complex computations more rapidly. This capability allows for real-time data processing, which is crucial for applications in artificial intelligence, robotics, and autonomous systems.
3. Improved Learning Capabilities
Neuromorphic computing systems excel in learning from data through mechanisms similar to biological learning. This adaptability enables these systems to recognize patterns and make decisions based on previous experiences, enhancing their performance in dynamic environments.
4. Scalability
The architecture of neuromorphic chips facilitates scaling to accommodate larger networks without a proportional increase in power consumption. This scalability supports the development of more sophisticated applications that require extensive data processing.
Neuromorphic computing has a variety of applications that leverage its brain-inspired architecture for efficient processing.
1. Artificial Intelligence
This technology is particularly useful in AI systems, enabling faster and more energy-efficient processing for tasks such as image and speech recognition.
2. Robotics
In robotics, neuromorphic computing allows for real-time sensory processing, enhancing the ability to perceive and react to complex environments.
3. Edge Computing
It is also ideal for edge devices, where low power consumption and high-speed data processing are essential, facilitating smart devices and IoT applications.
4. Autonomous Vehicles
Neuromorphic systems can support autonomous vehicles by providing the necessary computational power for real-time decision-making based on sensory input.
5. Healthcare
In healthcare, this technology aids in processing large amounts of biological data, improving diagnostics and personalized treatment plans.
These applications highlight the potential of neuromorphic computing to revolutionize various industries through enhanced performance and efficiency.
Neuromorphic computing mimics the architecture and functioning of the human brain, enabling it to process information in a way that is more analogous to biological neural networks. This approach uses specialized hardware designed to emulate the behavior of neurons and synapses, allowing for more efficient data processing and learning capabilities. In contrast, traditional computing relies on a linear processing model, where tasks are executed sequentially using binary logic. The differences also extend to power consumption and speed; neuromorphic systems are often more energy-efficient and capable of parallel processing, which allows them to handle complex tasks like pattern recognition and sensory processing much more effectively than conventional computers. This fundamental shift in design and operation positions neuromorphic computing as a promising solution for applications in artificial intelligence and machine learning.
Some interesting numbers and facts about your company results for Neuromorphic Computing
| Country with most fitting companies | Germany |
| Amount of fitting manufacturers | 7663 |
| Amount of suitable service providers | 5783 |
| Average amount of employees | 11-50 |
| Oldest suiting company | 2006 |
| Youngest suiting company | 2021 |
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Some interesting questions that has been asked about the results you have just received for Neuromorphic Computing
What are related technologies to Neuromorphic Computing?
Based on our calculations related technologies to Neuromorphic Computing are Biomedical (Red), Bioinformatics (Gold), Environmental Biotechnology (Grey), Agricultural Biotechnology (Green), Food Related Biotechnology (Yellow)
Who are Start-Ups in the field of Neuromorphic Computing?
Start-Ups who are working in Neuromorphic Computing are EU H2020 NEURONN, Axelera AI
Which industries are mostly working on Neuromorphic Computing?
The most represented industries which are working in Neuromorphic Computing are IT, Software and Services, Other, Electronics and Electrical engineering, Semiconductor, Biotechnology
How does ensun find these Neuromorphic Computing Companies?
ensun uses an advanced search and ranking system capable of sifting through millions of companies and hundreds of millions of products and services to identify suitable matches. This is achieved by leveraging cutting-edge technologies, including Artificial Intelligence.
