Smoothsailing
Regular
Might pay to open my eyes you recon
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Luppo71
Founding Member
Its pretty simple, your post insinuated that if i didn't put people on ignore then i might know what has been posted.
i wasn't having a go at you, my comment was nothing more than that, you are the only person i have thought about ignoring.
Conversation over.
ANT61 on LinkedIn: Last month, we had the honour of presenting our neuromorphic technology to…
Last month, we had the honour of presenting our neuromorphic technology to edge-computing and in-orbit servicing program leaders at ESA/ESTEC. It's a…
DingoBorat
Slim
I think it's just a cultural thing SC..We're all a little frustrated. All good. Just.pointing out it is hard to understand what someone means in written word on a forum. Unless of course someone fires both barrels at someone and leaves no doubt.
SC
I tried Googling "German comedians" and this is all that came up
..
ANT61 on LinkedIn: Last month, we had the honour of presenting our neuromorphic technology to…
Last month, we had the honour of presenting our neuromorphic technology to edge-computing and in-orbit servicing program leaders at ESA/ESTEC. It's a…www.linkedin.com
View attachment 68741 View attachment 68742 View attachment 68743
Does anyone else get the feeling this snowball is starting to roll?
7für7
Top 20
It's not my fault that you took my first post so seriously. I even added smileys, but it seems like you have a problem with me. Just ignore me if you want peace of mind, like 3/4 of the forum does. So, let's consider the topic closed now
Pom down under
Top 20
So glad I only use picturesWe're all a little frustrated. All good. Just.pointing out it is hard to understand what someone means in written word on a forum. Unless of course someone fires both barrels at someone and leaves no doubt.
SC
Fullmoonfever
Top 20
Be nice if they were speaking to us...might help them.
At least they acknowledging the potential power of SNN as they put it and are exploring ...it's a start.
By Dongsheng Li , Principal Research Manager Dongqi Han , Researcher Yansen Wang , Researcher
As AI research and technology development continue to advance, there is also a need to account for the energy and infrastructure resources required to manage large datasets and execute difficult computations. When we look to nature for models of efficiency, the human brain stands out, resourcefully handling complex tasks. Inspired by this, researchers at Microsoft are seeking to understand the brain’s efficient processes and replicate them in AI.
At Microsoft Research Asia(opens in new tab), in collaboration with Fudan University(opens in new tab), Shanghai Jiao Tong University(opens in new tab), and the Okinawa Institute of Technology(opens in new tab), three notable projects are underway. One introduces a neural network that simulates the way the brain learns and computes information; another enhances the accuracy and efficiency of predictive models for future events; and a third improves AI’s proficiency in language processing and pattern prediction. These projects, highlighted in this blog post, aim not only to boost performance but also significantly reduce power consumption, paving the way for more sustainable AI technologies.
Figure 1: The four neural connectivity patterns in the brain. Each circle represents a neuron, and each arrow represents a synapse.
Inspired by this biological architecture, researchers have developed CircuitNet, a neural network that replicates multiple types of connectivity patterns. CircuitNet’s design features a combination of densely connected local nodes and fewer connections between distant regions, enabling enhanced signal transmission through circuit motif units (CMUs)—small, recurring patterns of connections that help to process information. This structure, shown in Figure 2, supports multiple rounds of signal processing, potentially advancing how AI systems handle complex information.
Figure 2. CircuitNet’s architecture: A generic neural network performs various tasks, accepts different inputs, and generates corresponding outputs (left). CMUs keep most connections local with few long-distance connections, promoting efficiency (middle). Each CMU has densely interconnected neurons to model universal circuit patterns (right).
Evaluation results are promising. CircuitNet outperformed several popular neural network architectures in function approximation, reinforcement learning, image classification, and time-series prediction. It also achieved comparable or better performance than other neural networks, often with fewer parameters, demonstrating its effectiveness and strong generalization capabilities across various machine learning tasks. Our next step is to test CircuitNet’s performance on large-scale models with billions of parameters.
To improve SNN’s predictive capabilities, researchers have proposed an SNN framework designed to predict trends over time, such as electricity consumption or traffic patterns. This approach utilizes the efficiency of spiking neurons in processing temporal information and synchronizes time-series data—collected at regular intervals—and SNNs. Two encoding layers transform the time-series data into spike sequences, allowing the SNNs to process them and make accurate predictions, shown in Figure 3.
Figure 3. A new framework for SNN-based time-series prediction: Time series data is encoded into spikes using a novel spike encoder (middle, bottom). The spikes are then processed by SNN models (Spike-TCN, Spike-RNN, and Spike-Transformer) for learning (top). Finally, the learned features are fed into the projection layer for prediction (bottom-right).
Tests show that this SNN approach is very effective for time-series prediction, often matching or outperforming traditional methods while significantly reducing energy consumption. SNNs successfully capture temporal dependencies and model time-series dynamics, offering an energy-efficient approach closely aligns with how the brain processes information. We plan to continue exploring ways to further improve SNNs based on the way the brain processes information.
Figure 4: Application of CPG-PE in an SNN. X, X′, and X-output are spike matrices.
We evaluated CPG-PE using four real-world datasets: two covering traffic patterns, and one each for electricity consumption and solar energy. Results demonstrate that SNNs using this method significantly outperform those without positional encoding (PE), shown in Table 1. Moreover, CPG-PE can be easily integrated into any SNN designed for sequence processing, making it adaptable to a wide range of neuromorphic chips and SNN hardware.
Table 1: Evaluation results of time-series forecasting on two benchmarks with prediction lengths 6, 24, 48, 96. “Metr-la” and “Pems-bay” are traffic-pattern datasets. The best SNN results are in bold. The up-arrows indicate a higher score, representing better performance.
At least they acknowledging the potential power of SNN as they put it and are exploring ...it's a start.
Brain-inspired design for more capable and sustainable AI
Researchers and their collaborators are drawing inspiration from the brain to develop more sustainable AI models. Projects like CircuitNet and CPG-PE improve performance and energy efficiency by mimicking the brain's neural patterns:
www.microsoft.com
Microsoft Research Blog
Innovations in AI: Brain-inspired design for more capable and sustainable technology
Published August 29, 2024By Dongsheng Li , Principal Research Manager Dongqi Han , Researcher Yansen Wang , Researcher
As AI research and technology development continue to advance, there is also a need to account for the energy and infrastructure resources required to manage large datasets and execute difficult computations. When we look to nature for models of efficiency, the human brain stands out, resourcefully handling complex tasks. Inspired by this, researchers at Microsoft are seeking to understand the brain’s efficient processes and replicate them in AI.
At Microsoft Research Asia(opens in new tab), in collaboration with Fudan University(opens in new tab), Shanghai Jiao Tong University(opens in new tab), and the Okinawa Institute of Technology(opens in new tab), three notable projects are underway. One introduces a neural network that simulates the way the brain learns and computes information; another enhances the accuracy and efficiency of predictive models for future events; and a third improves AI’s proficiency in language processing and pattern prediction. These projects, highlighted in this blog post, aim not only to boost performance but also significantly reduce power consumption, paving the way for more sustainable AI technologies.
CircuitNet simulates brain-like neural patterns
Many AI applications rely on artificial neural networks, designed to mimic the brain’s complex neural patterns. These networks typically replicate only one or two types of connectivity patterns. In contrast, the brain propagates information using a variety of neural connection patterns, including feedforward excitation and inhibition, mutual inhibition, lateral inhibition, and feedback inhibition (Figure 1). These networks contain densely interconnected local areas with fewer connections between distant regions. Each neuron forms thousands of synapses to carry out specific tasks within its region, while some synapses link different functional clusters—groups of interconnected neurons that work together to perform specific functions.
Inspired by this biological architecture, researchers have developed CircuitNet, a neural network that replicates multiple types of connectivity patterns. CircuitNet’s design features a combination of densely connected local nodes and fewer connections between distant regions, enabling enhanced signal transmission through circuit motif units (CMUs)—small, recurring patterns of connections that help to process information. This structure, shown in Figure 2, supports multiple rounds of signal processing, potentially advancing how AI systems handle complex information.
Evaluation results are promising. CircuitNet outperformed several popular neural network architectures in function approximation, reinforcement learning, image classification, and time-series prediction. It also achieved comparable or better performance than other neural networks, often with fewer parameters, demonstrating its effectiveness and strong generalization capabilities across various machine learning tasks. Our next step is to test CircuitNet’s performance on large-scale models with billions of parameters.
Spiking neural networks: A new framework for time-series prediction
Spiking neural networks (SNNs) are emerging as a powerful type of artificial neural network, noted for their energy efficiency and potential application in fields like robotics, edge computing, and real-time processing. Unlike traditional neural networks, which process signals continuously, SNNs activate neurons only upon reaching a specific threshold, generating spikes. This approach simulates the way the brain processes information and conserves energy. However, SNNs are not strong at predicting future events based on historical data, a key function in sectors like transportation and energy.To improve SNN’s predictive capabilities, researchers have proposed an SNN framework designed to predict trends over time, such as electricity consumption or traffic patterns. This approach utilizes the efficiency of spiking neurons in processing temporal information and synchronizes time-series data—collected at regular intervals—and SNNs. Two encoding layers transform the time-series data into spike sequences, allowing the SNNs to process them and make accurate predictions, shown in Figure 3.
Tests show that this SNN approach is very effective for time-series prediction, often matching or outperforming traditional methods while significantly reducing energy consumption. SNNs successfully capture temporal dependencies and model time-series dynamics, offering an energy-efficient approach closely aligns with how the brain processes information. We plan to continue exploring ways to further improve SNNs based on the way the brain processes information.
Refining SNN sequence prediction
While SNNs can help models predict future events, research has shown that its reliance on spike-based communication makes it challenging to directly apply many techniques from artificial neural networks. For example, SNNs struggle to effectively process rhythmic and periodic patterns found in natural language processing and time-series analysis. In response, researchers developed a new approach for SNNs called CPG-PE, which combines two techniques:- Central pattern generators (CPGs): Neural networks in the brainstem and spinal cord that autonomously generate rhythmic patterns, controlling function like moving, breathing, and chewing
- Positional encoding (PE): A process that helps artificial neural networks discern the order and relative positions of elements within a sequence
We evaluated CPG-PE using four real-world datasets: two covering traffic patterns, and one each for electricity consumption and solar energy. Results demonstrate that SNNs using this method significantly outperform those without positional encoding (PE), shown in Table 1. Moreover, CPG-PE can be easily integrated into any SNN designed for sequence processing, making it adaptable to a wide range of neuromorphic chips and SNN hardware.
Ongoing AI research for greater capability, efficiency, and sustainability
The innovations highlighted in this blog demonstrate the potential to create AI that is not only more capable but also more efficient. Looking ahead, we’re excited to deepen our collaborations and continue applying insights from neuroscience to AI research, continuing our commitment to exploring ways to develop more sustainable technology.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Be nice if they were speaking to us...might help them.
At least they acknowledging the potential power of SNN as they put it and are exploring ...it's a start.
Brain-inspired design for more capable and sustainable AI
Researchers and their collaborators are drawing inspiration from the brain to develop more sustainable AI models. Projects like CircuitNet and CPG-PE improve performance and energy efficiency by mimicking the brain's neural patterns:www.microsoft.com
Microsoft Research Blog
Innovations in AI: Brain-inspired design for more capable and sustainable technology
Published August 29, 2024
By Dongsheng Li , Principal Research Manager Dongqi Han , Researcher Yansen Wang , Researcher
As AI research and technology development continue to advance, there is also a need to account for the energy and infrastructure resources required to manage large datasets and execute difficult computations. When we look to nature for models of efficiency, the human brain stands out, resourcefully handling complex tasks. Inspired by this, researchers at Microsoft are seeking to understand the brain’s efficient processes and replicate them in AI.
At Microsoft Research Asia(opens in new tab), in collaboration with Fudan University(opens in new tab), Shanghai Jiao Tong University(opens in new tab), and the Okinawa Institute of Technology(opens in new tab), three notable projects are underway. One introduces a neural network that simulates the way the brain learns and computes information; another enhances the accuracy and efficiency of predictive models for future events; and a third improves AI’s proficiency in language processing and pattern prediction. These projects, highlighted in this blog post, aim not only to boost performance but also significantly reduce power consumption, paving the way for more sustainable AI technologies.
CircuitNet simulates brain-like neural patterns
Many AI applications rely on artificial neural networks, designed to mimic the brain’s complex neural patterns. These networks typically replicate only one or two types of connectivity patterns. In contrast, the brain propagates information using a variety of neural connection patterns, including feedforward excitation and inhibition, mutual inhibition, lateral inhibition, and feedback inhibition (Figure 1). These networks contain densely interconnected local areas with fewer connections between distant regions. Each neuron forms thousands of synapses to carry out specific tasks within its region, while some synapses link different functional clusters—groups of interconnected neurons that work together to perform specific functions.
Figure 1: The four neural connectivity patterns in the brain. Each circle represents a neuron, and each arrow represents a synapse.
Inspired by this biological architecture, researchers have developed CircuitNet, a neural network that replicates multiple types of connectivity patterns. CircuitNet’s design features a combination of densely connected local nodes and fewer connections between distant regions, enabling enhanced signal transmission through circuit motif units (CMUs)—small, recurring patterns of connections that help to process information. This structure, shown in Figure 2, supports multiple rounds of signal processing, potentially advancing how AI systems handle complex information.
Figure 2. CircuitNet’s architecture: A generic neural network performs various tasks, accepts different inputs, and generates corresponding outputs (left). CMUs keep most connections local with few long-distance connections, promoting efficiency (middle). Each CMU has densely interconnected neurons to model universal circuit patterns (right).
Evaluation results are promising. CircuitNet outperformed several popular neural network architectures in function approximation, reinforcement learning, image classification, and time-series prediction. It also achieved comparable or better performance than other neural networks, often with fewer parameters, demonstrating its effectiveness and strong generalization capabilities across various machine learning tasks. Our next step is to test CircuitNet’s performance on large-scale models with billions of parameters.
Spiking neural networks: A new framework for time-series prediction
Spiking neural networks (SNNs) are emerging as a powerful type of artificial neural network, noted for their energy efficiency and potential application in fields like robotics, edge computing, and real-time processing. Unlike traditional neural networks, which process signals continuously, SNNs activate neurons only upon reaching a specific threshold, generating spikes. This approach simulates the way the brain processes information and conserves energy. However, SNNs are not strong at predicting future events based on historical data, a key function in sectors like transportation and energy.
To improve SNN’s predictive capabilities, researchers have proposed an SNN framework designed to predict trends over time, such as electricity consumption or traffic patterns. This approach utilizes the efficiency of spiking neurons in processing temporal information and synchronizes time-series data—collected at regular intervals—and SNNs. Two encoding layers transform the time-series data into spike sequences, allowing the SNNs to process them and make accurate predictions, shown in Figure 3.
Figure 3. A new framework for SNN-based time-series prediction: Time series data is encoded into spikes using a novel spike encoder (middle, bottom). The spikes are then processed by SNN models (Spike-TCN, Spike-RNN, and Spike-Transformer) for learning (top). Finally, the learned features are fed into the projection layer for prediction (bottom-right).
Tests show that this SNN approach is very effective for time-series prediction, often matching or outperforming traditional methods while significantly reducing energy consumption. SNNs successfully capture temporal dependencies and model time-series dynamics, offering an energy-efficient approach closely aligns with how the brain processes information. We plan to continue exploring ways to further improve SNNs based on the way the brain processes information.
Refining SNN sequence prediction
While SNNs can help models predict future events, research has shown that its reliance on spike-based communication makes it challenging to directly apply many techniques from artificial neural networks. For example, SNNs struggle to effectively process rhythmic and periodic patterns found in natural language processing and time-series analysis. In response, researchers developed a new approach for SNNs called CPG-PE, which combines two techniques:
By integrating these two techniques, CPG-PE helps SNNs discern the position and timing of signals, improving their ability to process time-based information. This process is shown in Figure 4.
- Central pattern generators (CPGs): Neural networks in the brainstem and spinal cord that autonomously generate rhythmic patterns, controlling function like moving, breathing, and chewing
- Positional encoding (PE): A process that helps artificial neural networks discern the order and relative positions of elements within a sequence
Figure 4: Application of CPG-PE in an SNN. X, X′, and X-output are spike matrices.
We evaluated CPG-PE using four real-world datasets: two covering traffic patterns, and one each for electricity consumption and solar energy. Results demonstrate that SNNs using this method significantly outperform those without positional encoding (PE), shown in Table 1. Moreover, CPG-PE can be easily integrated into any SNN designed for sequence processing, making it adaptable to a wide range of neuromorphic chips and SNN hardware.
Table 1: Evaluation results of time-series forecasting on two benchmarks with prediction lengths 6, 24, 48, 96. “Metr-la” and “Pems-bay” are traffic-pattern datasets. The best SNN results are in bold. The up-arrows indicate a higher score, representing better performance.
Ongoing AI research for greater capability, efficiency, and sustainability
The innovations highlighted in this blog demonstrate the potential to create AI that is not only more capable but also more efficient. Looking ahead, we’re excited to deepen our collaborations and continue applying insights from neuroscience to AI research, continuing our commitment to exploring ways to develop more sustainable technology.
Here's another example of Microsoft researching SNN's. Research paper published Feb 2024.
This one looks like it would be a perfect fit with incorporation of TENNs.
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks - Microsoft Research
Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, provide a unique pathway for capturing the intricacies of temporal data. However, applying SNNs to time-series forecasting is challenging due to difficulties in effective temporal alignment, complexities in...
www.microsoft.com
Last edited:
Bravo
If ARM was an arm, BRN would be its biceps💪!
Published Sat, Aug 31, 2024,
finance.yahoo.com
Extract
Arm Holdings plc (NASDAQ:ARM): Dominating Compute Market
We recently published a list of 17 Trending AI Stocks on Latest Analyst Ratings and News. In this article, we are going to take a look at where Arm Holdings plc (NASDAQ:ARM) stands against the other trending AI stocks. The AI industry continues to be highly dynamic, with significant growth...
Extract
Bravo
If ARM was an arm, BRN would be its biceps💪!
KAIST strikes again! 
The "heterovalent ion doping" method??
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Neuromorphic computing technology, which mimics the human brain to implement artificial intelligence (AI) operations. (Photo by Getty Images Bank)
A team of South Korean researchers has developed a technology that enhances the reliability and commercialization of next-generation neuromorphic computing devices by addressing their irregular characteristics.
Professor Choi Sin-hyeon from the Korea Advanced Institute of Science and Technology (KAIST) and his team, in collaboration with researchers from Hanyang University, announced on June 21 that they have developed a heterovalent ion doping method that improves the reliability and performance of next-generation memory devices.
Neuromorphic computing is a technology that implements AI operations by emulating the human brain. It uses memristors as basic units, which are advantageous for their low power consumption, high integration, and efficiency. Memristors, a portmanteau of memory and resistor, are memory devices that retain all previous states. However, due to their unstable characteristics, memristors often have low reliability.
The research team developed a “Heterovalent ion doping method” to improve the uniformity and performance of these devices. Heterovalent ions are ions that have a different valency from the atoms that originally existed, with valency being a measure of bonding.
The team proved the performance of heterovalent ion doping through atomic-level simulation analysis. The doped heterovalent ions attracted vacancies in nearby oxygen, creating stable device operation. Additionally, the space near these ions was expanded, allowing for faster device operation. According to the team's analysis, the performance of memristors doped with heterovalent ions improved in both crystalline and amorphous environments.
Professor Choi Sin-hyeon stated, "The heterovalent ion doping method can enhance the reliability and performance of neuromorphic devices,” and added, “It can contribute to the commercialization of next-generation neuromorphic computing based on memristors."
The results of this study were published in the international academic journal “Science Advances” on June 7.
www.businesskorea.co.kr
The "heterovalent ion doping" method??
Korean Researchers Discover Method to Enhance Next-Gen. Neuromorphic Computer Performance
- Editor Jasmine Choi
- 2024.06.21 16:52
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facebook(으)로 기사보내기 twitter(으)로 기사보내기 URL Copy(으)로 기사보내기 링크드인(으)로 기사보내기 Send to Email Share Scrap
Neuromorphic computing technology, which mimics the human brain to implement artificial intelligence (AI) operations. (Photo by Getty Images Bank)
A team of South Korean researchers has developed a technology that enhances the reliability and commercialization of next-generation neuromorphic computing devices by addressing their irregular characteristics.
Professor Choi Sin-hyeon from the Korea Advanced Institute of Science and Technology (KAIST) and his team, in collaboration with researchers from Hanyang University, announced on June 21 that they have developed a heterovalent ion doping method that improves the reliability and performance of next-generation memory devices.
Neuromorphic computing is a technology that implements AI operations by emulating the human brain. It uses memristors as basic units, which are advantageous for their low power consumption, high integration, and efficiency. Memristors, a portmanteau of memory and resistor, are memory devices that retain all previous states. However, due to their unstable characteristics, memristors often have low reliability.
The research team developed a “Heterovalent ion doping method” to improve the uniformity and performance of these devices. Heterovalent ions are ions that have a different valency from the atoms that originally existed, with valency being a measure of bonding.
The team proved the performance of heterovalent ion doping through atomic-level simulation analysis. The doped heterovalent ions attracted vacancies in nearby oxygen, creating stable device operation. Additionally, the space near these ions was expanded, allowing for faster device operation. According to the team's analysis, the performance of memristors doped with heterovalent ions improved in both crystalline and amorphous environments.
Professor Choi Sin-hyeon stated, "The heterovalent ion doping method can enhance the reliability and performance of neuromorphic devices,” and added, “It can contribute to the commercialization of next-generation neuromorphic computing based on memristors."
The results of this study were published in the international academic journal “Science Advances” on June 7.
Korean Researchers Discover Method to Enhance Next-Gen. Neuromorphic Computer Performance
A team of South Korean researchers has developed a technology that enhances the reliability and commercialization of next-generation neuromorphic computing dev
HopalongPetrovski
I'm Spartacus!
Ah, of course. The old atomic-level simulation analysis trick.KAIST strikes again!
The "heterovalent ion doping" method??
View attachment 68757
Korean Researchers Discover Method to Enhance Next-Gen. Neuromorphic Computer Performance
- Editor Jasmine Choi
- 2024.06.21 16:52
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facebook(으)로 기사보내기 twitter(으)로 기사보내기 URL Copy(으)로 기사보내기 링크드인(으)로 기사보내기 Send to Email Share Scrap
Neuromorphic computing technology, which mimics the human brain to implement artificial intelligence (AI) operations. (Photo by Getty Images Bank)
A team of South Korean researchers has developed a technology that enhances the reliability and commercialization of next-generation neuromorphic computing devices by addressing their irregular characteristics.
Professor Choi Sin-hyeon from the Korea Advanced Institute of Science and Technology (KAIST) and his team, in collaboration with researchers from Hanyang University, announced on June 21 that they have developed a heterovalent ion doping method that improves the reliability and performance of next-generation memory devices.
Neuromorphic computing is a technology that implements AI operations by emulating the human brain. It uses memristors as basic units, which are advantageous for their low power consumption, high integration, and efficiency. Memristors, a portmanteau of memory and resistor, are memory devices that retain all previous states. However, due to their unstable characteristics, memristors often have low reliability.
The research team developed a “Heterovalent ion doping method” to improve the uniformity and performance of these devices. Heterovalent ions are ions that have a different valency from the atoms that originally existed, with valency being a measure of bonding.
The team proved the performance of heterovalent ion doping through atomic-level simulation analysis. The doped heterovalent ions attracted vacancies in nearby oxygen, creating stable device operation. Additionally, the space near these ions was expanded, allowing for faster device operation. According to the team's analysis, the performance of memristors doped with heterovalent ions improved in both crystalline and amorphous environments.
Professor Choi Sin-hyeon stated, "The heterovalent ion doping method can enhance the reliability and performance of neuromorphic devices,” and added, “It can contribute to the commercialization of next-generation neuromorphic computing based on memristors."
The results of this study were published in the international academic journal “Science Advances” on June 7.
Korean Researchers Discover Method to Enhance Next-Gen. Neuromorphic Computer Performance
A team of South Korean researchers has developed a technology that enhances the reliability and commercialization of next-generation neuromorphic computing dev
By Jove, why didn't we think of that???
Time to send in a gunboat!!! What!!!
CHIPS
Regular
Weebit ReRAM Applications | Weebit
Since nearly every electronic product requires NVM, the applications for Weebit ReRAM (RRAM) are numerous and varied. Read about our applications
www.weebit-nano.com
Weebit ReRAM Applications | Weebit
Since nearly every electronic product requires NVM, the applications for Weebit ReRAM (RRAM) are numerous and varied. Read about our applications
www.weebit-nano.com
Neuromorphic Computing
The use of Neural Networks is leading to exciting developments in artificial intelligence, with the ability to increase machine learning accuracy in areas like speech recognition and image classification.
However, reaching this high degree of precision using traditional computing architectures takes a significant power toll. Since much of this power consumption is related to data movement between a system’s computing elements and memory modules, the industry is investigating new technologies that can reduce this data movement. The solution lies in integrating dense, low-power NVM closer to the computing elements – also called In-Memory Computing - and Weebit ReRAM is an ideal candidate.In addition, the next wave of AI and Machine Learning architectures will take a new approach – Analog Computing, or Neuromorphic Computing – whereby the computation is done within the storage element in an analog fashion. These architectures are designed to accurately emulate the brain’s operation and can therefore achieve orders of magnitude better power efficiency.
The Weebit ReRAM cell functions similarly to a synapse in the brain, making it a promising solution for neuromorphic computing. Many institutes are now studying this domain, which has the potential to become a huge market in the future.
We are collaborating with research partners – both academia and industry – to explore the possibilities of using ReRAM for neuromorphic computing. This includes ongoing projects with the Non-Volatile Memory Research Group of the Indian Institute of Technology Delhi (IITD), the Institute of Nanoscience and Nanotechnology (INN) NCSR ‘Demokritos’, the Politecnico di Milano and the Technion – Israel Institute of Technology, as well as CEA-Leti. Together with Leti we were the first in the industry to demonstrate ReRAM-based spiking neural networks (SNN).
DingoBorat
Slim
Maybe they should just try digital..KAIST strikes again!
The "heterovalent ion doping" method??
View attachment 68757
Korean Researchers Discover Method to Enhance Next-Gen. Neuromorphic Computer Performance
- Editor Jasmine Choi
- 2024.06.21 16:52
Print URL Copy Fonts Size Down Fonts Size Up
facebook(으)로 기사보내기 twitter(으)로 기사보내기 URL Copy(으)로 기사보내기 링크드인(으)로 기사보내기 Send to Email Share Scrap
Neuromorphic computing technology, which mimics the human brain to implement artificial intelligence (AI) operations. (Photo by Getty Images Bank)
A team of South Korean researchers has developed a technology that enhances the reliability and commercialization of next-generation neuromorphic computing devices by addressing their irregular characteristics.
Professor Choi Sin-hyeon from the Korea Advanced Institute of Science and Technology (KAIST) and his team, in collaboration with researchers from Hanyang University, announced on June 21 that they have developed a heterovalent ion doping method that improves the reliability and performance of next-generation memory devices.
Neuromorphic computing is a technology that implements AI operations by emulating the human brain. It uses memristors as basic units, which are advantageous for their low power consumption, high integration, and efficiency. Memristors, a portmanteau of memory and resistor, are memory devices that retain all previous states. However, due to their unstable characteristics, memristors often have low reliability.
The research team developed a “Heterovalent ion doping method” to improve the uniformity and performance of these devices. Heterovalent ions are ions that have a different valency from the atoms that originally existed, with valency being a measure of bonding.
The team proved the performance of heterovalent ion doping through atomic-level simulation analysis. The doped heterovalent ions attracted vacancies in nearby oxygen, creating stable device operation. Additionally, the space near these ions was expanded, allowing for faster device operation. According to the team's analysis, the performance of memristors doped with heterovalent ions improved in both crystalline and amorphous environments.
Professor Choi Sin-hyeon stated, "The heterovalent ion doping method can enhance the reliability and performance of neuromorphic devices,” and added, “It can contribute to the commercialization of next-generation neuromorphic computing based on memristors."
The results of this study were published in the international academic journal “Science Advances” on June 7.
Korean Researchers Discover Method to Enhance Next-Gen. Neuromorphic Computer Performance
A team of South Korean researchers has developed a technology that enhances the reliability and commercialization of next-generation neuromorphic computing dev
Has anyone grabbed the patent for that?
..
supersonic001
Regular
Published Sat, Aug 31, 2024,
Arm Holdings plc (NASDAQ:ARM): Dominating Compute Market
We recently published a list of 17 Trending AI Stocks on Latest Analyst Ratings and News. In this article, we are going to take a look at where Arm Holdings plc (NASDAQ:ARM) stands against the other trending AI stocks. The AI industry continues to be highly dynamic, with significant growth...
Extract
View attachment 68756
…sounds like catchup, we at BRN are familiar in setting these trends, only now slowly being understood by others in industry…..!
GO Chippa !
DingoBorat
Slim
"This year, we are deploying a limited number of NEO units in selected homes for research and development purposes"
Ahead of Tesla, in that respect, who is currently only using Optimus, in his factories, as far as I'm aware..
Thought this was some kind of skit, just to make me depressed, before finding the announcement..
Last edited:
Ask yourself, why would an employee from Qualcomm repost this.......
Happy Fathers Day
www.linkedin.com
Happy Fathers Day
BrainChip on LinkedIn: Upcoming Event: The BrainChip team is looking forward to participating in…
Upcoming Event: The BrainChip team is looking forward to participating in the AI Hardware & Edge AI Summit, September 9-12, 2024, at the Signia by Hilton, San…
Last edited:
Smoothsailing
Regular
Very good news
But but, I posted the Lamborghini website to some friends.Ask yourself, why would an employee from Qualcomm repost this.......
Happy Fathers Day
View attachment 68775 View attachment 68776BrainChip on LinkedIn: Upcoming Event: The BrainChip team is looking forward to participating in…
Upcoming Event: The BrainChip team is looking forward to participating in the AI Hardware & Edge AI Summit, September 9-12, 2024, at the Signia by Hilton, San…
Even if BRN ends up at $100 a share, I would never buy one, ever!
I can't take those private linkedin posts as dot joining to be honest, they MIGHT BE, but it's far from given.
Here is the Lambo web page so you have something nice to look at on a Sunday Father's Day, LOL.
Automobili Lamborghini - Official Website | Lamborghini.com
Lamborghini Official Website: find Lamborghini models, new releases, latest news, events, and the dealers across the world
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