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NimbleAI.eu
Arrasate / Mondragón, Spain
A
51-100 Employees
-
Key takeaway
NimbleAI is developing an innovative neuromorphic chip that integrates sensing and processing, taking inspiration from the energy-efficient light detection in eyes and the visual information processing in brains. This 3D integrated chip 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
Neuronspike
Boston, United States
B
1-10 Employees
-
Key takeaway
Neuronspike Technologies is focused on developing brain-inspired AI models and chipsets, which aligns with the concept of neuromorphic chips. Their mission is to create hardware-aware Artificial General Intelligence (AGI) that can be efficiently deployed on edge devices.
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.
BrainChip
Aliso Viejo, United States
B
11-50 Employees
2006
Key takeaway
BrainChip is a leader in edge AI on-chip processing, specializing in making devices AI-smart through neuromorphic computing techniques. Their innovative approach includes event-based processing and on-chip learning, enabling efficient sensor data interpretation and enhanced machine intelligence.
Reference
Product
Technology - BrainChip
Explore all the incredible technology Brainchip has to offer!
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Netrasemi
Thiruvananthapuram, India
D
11-50 Employees
2020
Key takeaway
Netrasemi's NetraSoC family addresses on-device AI computing needs, particularly for IoT applications, with their Deep Neural Processor (NetraDNPU) and specialized hardware engines. Their architecture offers high energy efficiency and seamless data flow, making it well-suited for edge computing in small and tiny devices.
Reference
Core business
Netrasemi | Intelligent Silicon solutions | AI/ML chips | Thiruvananthapuram
Netrasemi is a semiconductor company providing domain specific SoC solutions with advanced AI/ML and hardware acceleration capabilities. Our SoC solutions enable you to build custom intelligent chips in record time and budget and help your accelerate your solution business with a chip advantage.
GrAI Matter Labs
Paris, France
A
51-100 Employees
2016
Key takeaway
GrAI Matter Labs specializes in brain-inspired neuromorphic chips that emulate human-like processing. Their GrAI VIP edge AI processor utilizes innovative NeuronFlow™ technology, enabling efficient and low-power computation, particularly suited for applications in robotics, AR/VR, and more.
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.
MemryX
Ann Arbor, United States
B
11-50 Employees
2019
Key takeaway
MemryX Inc is developing innovative Edge AI solutions that utilize a proprietary, configurable native dataflow architecture and at-memory computing, positioning them as a key player in neuromorphic computing. Their MemryX MX3 AI accelerator card exemplifies their commitment to optimizing AI model performance with advanced technology designed for efficient processing.
Reference
Product
Technology - MemryX
Chip makers are always making design tradeoffs to balance performance, power, area, cost, quality, size, thermals, compatibility, efficiency, ease of use, and
Neural Semiconductor Limited
Dhaka, Bangladesh
E
51-100 Employees
2017
Key takeaway
Neural Semiconductor Limited (NSL) is dedicated to supporting the semiconductor manufacturing industry, which may align with the development of neuromorphic chips. Their focus on creating a robust ecosystem for semiconductor design and manufacturing in Bangladesh suggests potential capabilities in advanced semiconductor technologies.
Reference
Core business
Neural Semiconductor Limited -
Corticale Srl
Genoa, Italy
B
1-10 Employees
2021
Key takeaway
Corticale is a pioneering neurotech company focused on developing advanced neuroelectronic devices and high-definition neurointerfacing components, which are essential for advancing neuroprosthetics and therapies related to brain disorders. Their innovative technologies and partnerships with leading firms in the neurotech market position them at the forefront of brain research and medical applications.
Reference
Core business
Corticale | OEM Neurotech Company
NovuMind
United States
B
11-50 Employees
2015
Key takeaway
NovuMind has developed the NovuTensor chip, which is designed specifically for tensor computation, offering significant efficiency and performance advantages for AI applications. This innovative approach addresses the computational challenges of deep learning inference, making it suitable for real-time applications in power-constrained environments.
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
Ceremorphic Inc.
San Jose, United States
B
51-100 Employees
-
Key takeaway
Ceremorphic is developing an ultra-low power supercomputing chip utilizing its patented technology and multi-thread processing architecture, which may include innovations relevant to neuromorphic computing. Their focus on energy efficiency and advanced processing capabilities positions them as a key player in the high-performance computing sector.
Reference
Product
Technology - Ceremorphic
Highly differentiated technology for creating sustainable product portfolio Multi-thread Processors Security Processing Machine Learning Processors Analog Computing Reliable Circuits 3D Interconnects Graph Neural Processing Programmable Logic Low Power Memory Over the last five years, Ceremorphic has created differentiated technologies to create a sustainable product portfolio in the high-performance computing space for the future. The foundation […]
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A neuromorphic chip is designed to mimic the neural structure and functioning of the human brain. These chips utilize a network of artificial neurons and synapses to process information in a way that resembles biological processes. This architecture allows for highly efficient computation, particularly in tasks like sensory processing, pattern recognition, and adaptive learning. By leveraging parallel processing capabilities, neuromorphic chips can handle massive amounts of data while consuming significantly less power compared to traditional processors. Their unique design makes them especially suitable for applications in artificial intelligence, robotics, and real-time data analysis.
Neuromorphic chips mimic the way the human brain processes information. They utilize specialized circuits to create artificial neurons and synapses, allowing them to perform computations in a manner similar to biological systems. This architecture enables the chips to process sensory data efficiently and execute tasks like pattern recognition and learning with low power consumption. By leveraging event-driven computation, neuromorphic chips can respond dynamically to input stimuli. Instead of processing data in a sequential manner like traditional chips, they operate in parallel, which enhances their ability to handle complex, real-time tasks. The result is a more efficient approach to artificial intelligence, with potential applications in robotics, autonomous systems, and advanced computing.
1. Energy Efficiency
Neuromorphic chips are designed to mimic the human brain's architecture, which allows them to process information using significantly less energy than traditional computing systems. This efficiency is crucial for mobile and embedded applications, where battery life is paramount.
2. Real-time Processing
These chips excel in handling complex data, such as sensory inputs, in real-time. They can perform tasks like pattern recognition and decision-making swiftly, making them ideal for applications in robotics, autonomous vehicles, and artificial intelligence systems.
3. Scalability
Neuromorphic chips can be easily scaled to accommodate larger networks or more complex tasks. Their parallel processing capabilities allow for the simultaneous handling of multiple inputs, which enhances their performance in various applications.
4. Adaptability
The architecture of neuromorphic chips enables them to learn and adapt to new information over time. This ability is beneficial for evolving applications, such as smart devices and adaptive systems, which require continuous learning from their environment.
Neuromorphic chips are utilized across various applications that benefit from their brain-inspired architecture. One significant application is in artificial intelligence, where these chips enhance machine learning capabilities by processing information in a manner similar to human neurons. This leads to improved efficiency in tasks like image and speech recognition. Another important use is in robotics, where neuromorphic chips enable robots to learn and adapt to their environments in real-time, allowing for more autonomous and intelligent behavior. Additionally, they are employed in edge computing scenarios, where low power consumption and real-time processing are crucial, such as in smart devices and IoT applications.
Neuromorphic chips are designed to mimic the neural structures and functioning of the human brain, allowing for more efficient processing of information compared to traditional chips. These chips utilize spiking neural networks to process data in a way that is more analogous to human cognition, enabling them to perform tasks like pattern recognition and sensory processing with lower power consumption. In contrast, traditional chips rely on binary processing using transistors that execute instructions sequentially. This results in higher energy usage and limited adaptability to dynamic environments. Neuromorphic chips, therefore, offer advantages in speed and energy efficiency, particularly for applications in artificial intelligence and machine learning, where real-time data processing is critical.
Some interesting numbers and facts about your company results for Neuromorphic Chip
| Country with most fitting companies | United States |
| Amount of fitting manufacturers | 8278 |
| Amount of suitable service providers | 4859 |
| 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 Chip
What are related technologies to Neuromorphic Chip?
Based on our calculations related technologies to Neuromorphic Chip are Biomedical (Red), Bioinformatics (Gold), Environmental Biotechnology (Grey), Agricultural Biotechnology (Green), Food Related Biotechnology (Yellow)
Who are Start-Ups in the field of Neuromorphic Chip?
Start-Ups who are working in Neuromorphic Chip are Corticale Srl
Which industries are mostly working on Neuromorphic Chip?
The most represented industries which are working in Neuromorphic Chip are IT, Software and Services, Other, Electronics and Electrical engineering, Biotechnology, Semiconductor
How does ensun find these Neuromorphic Chip 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.
