Great find. Tata Elxsi are a will use AKIDA. Tony said at the last AGM one big deal would see us profitable overnight.
We also know about the Mercedes tie up.
Mmmmm its hard not to feel a little positive.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
We also know about the Mercedes tie up.
Mmmmm its hard not to feel a little positive.
Hope you do not mind but i pinched the Tata link you posted and dropped it over on the crapper just to 'put the wind up the downrampers'.
They will no doubt rapid fire to bury it. Usually after hours its one poster pulling out several alias accounts one after the other to do the burying.
Fullmoonfever
Top 20
Nice names to be next to....high risk high reward....red or black 
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The human brain consumes about 20 watts of power, which is roughly equivalent to a dim light bulb. Despite this low power consumption, it can perform an estimated 10^16 operations per second. In contrast, a high-end Nvidia GPU consumes about 400 watts of power to perform a “measly” 10^14 to 10^15 operations per second. This means the human brain is approximately 100,000 to 1,000,000 times more energy-efficient than modern GPUs.
But what if we could replicate that amazing efficiency on a physical computer chip? That’s the ambitious vision for neuromorphic computing. The neuromorphic computing market, while still in its infancy, is projected to grow by 21.2% annually (CAGR) over the next five years. So in this guide, we’ll explore the top neuromorphic computing stocks, ranked by pure-play focus.
The key growth drivers for the neuromorphic computing market stem from several high-impact sectors.. For example, the automotive industry is pushing for more advanced driver assistance systems and fully autonomous vehicles. These require real-time processing of vast amounts of sensory data with minimal latency and power consumption – an ideal use case for neuromorphic chips.
Mobile and edge AI is another huge demand driver. As AI capabilities become more important in smartphones and other mobile devices, neuromorphic chips offer a way to run sophisticated AI models without quickly draining batteries or requiring constant cloud connectivity.
That said, keep in the mind that neuromorphic computing is a bleeding edge technology. Creating chips that truly mimic the brain’s neuron and synapse structure at scale is an enormously complex task, and current neuromorphic chips are highly limited in their neuron count compared to the human brain. Also traditional software development approaches don’t translate directly to neuromorphic systems. This presents a major (and still unsolved) challenge in integration with existing computing ecosystems.
Note: We make every effort to keep our information accurate and up-to-date. However, technology markets do move fast and company situations can change rapidly. Please use this guide as an intro to the neuromorphic computing landscape; but ultimately, do your own due diligence before taking action.
Australia-based BrainChip is a pioneer in commercializing neuromorphic computing, focusing on edge AI solutions. The company has developed an Edge AI platform that combines innovative silicon IP, software, and machine learning. This platform includes the Akida neuromorphic processor. Akida is designed to process information in a way that mimics the human brain from a fundamental hardware level. This “imitation” goes beyond the deep neural networks used in today’s AI models.
Brainchip enjoys first-mover advantage in commercial neuromorphic computing. The company’s technology has several unique features, including microwatt power consumption and on-chip learning, while being able to support standard machine learning workflows. In fact, it offers a claimed 5-10x improvement in performance-per-watt over traditional AI accelerators. This would make the Akida chip ideal for battery-powered devices, edge computing, and in-sensor intelligence.
The company is pursuing a flexible business model centered on high-margin IP licensing. This strategy involves upfront license fees and ongoing royalties, which could provide steady revenue as adoption grows. BrainChip’s intellectual property portfolio includes 17 granted patents and 30 pending patents. The company’s team consists of 80% engineers, with 15% holding PhDs from leading AI research programs. BrainChip is also building partnerships with system integrators, including MegaChips, Prophesee, and SiFive.
Neuromorphic chips are ideal for machine vision, edge AI, and smart cities.
Intel’s Loihi neuromorphic chip represents a significant advancement in brain-inspired computing. The chip contains millions of artificial neurons and synapses, allowing it to process information in a manner similar to biological neural networks. This architecture enables Loihi to excel at tasks that involve learning and adapting to new information in real-time, a capability that traditional computing systems often struggle with.
One of Loihi’s key features is its energy efficiency. The chip can perform certain types of AI computations using up to 1,000 times less energy than conventional processors. This efficiency stems from its event-driven processing, where neurons only activate when they receive sufficient input, similar to how biological neurons function. This approach allows Loihi to handle complex cognitive tasks while consuming minimal power, making it particularly suitable for edge computing and IoT applications.
Loihi’s versatility has been evident in various research applications. Intel has used the chip to solve optimization problems, control robotic systems, and process sensory data. For example, Loihi has been applied to gesture recognition, real-time image classification, and even olfactory processing. The chip’s ability to learn and adapt quickly has also shown promise in creating AI systems that can operate effectively in dynamic, unpredictable environments. As Intel continues to refine and scale up its neuromorphic technology, Loihi could potentially bridge the gap between traditional computing and the complex, adaptive capabilities of biological brains.
IBM’s TrueNorth neuromorphic chip architecture represents a significant leap in brain-inspired computing. The chip consists of a network of neurosynaptic cores, each containing 256 neurons that can establish up to 256 connections with other neurons. This dense, interconnected structure allows TrueNorth to process information in a highly parallel manner, similar to biological neural networks. The architecture is particularly adept at handling sensory data and performing pattern recognition tasks.
TrueNorth’s energy efficiency is one of its standout features. The chip can perform complex cognitive tasks while consuming only a fraction of the power required by traditional processors. This efficiency is achieved through its event-driven design, where neurons only activate and consume energy when they receive sufficient input. As a result, TrueNorth can process billions of synaptic operations per second per watt, making it well-suited for deployment in power-constrained environments such as mobile devices and IoT sensors.
IBM has demonstrated TrueNorth’s capabilities in various real-world applications. The chip has been used for tasks such as real-time object recognition, audio processing, and complex data analysis. For instance, TrueNorth has been applied to analyze satellite imagery for disaster response, detect anomalies in financial transactions, and even assist in medical diagnosis. The chip’s ability to learn and adapt to new patterns makes it particularly useful in scenarios where data streams are constantly changing. As IBM continues to develop and scale its neuromorphic technology, TrueNorth could play a crucial role in advancing AI systems that can operate more efficiently and effectively in complex, real-world environments.
These phase change neurons store states in response to neuronal inputs. Credit: IBM Research
AMD, while primarily known for its CPUs and GPUs, could fit neuromorphic computing into its broader AI strategy. Keep in mind though, AMD’s current approach to neuromorphic computing is not publicized. The company is still focused more on general-purpose AI acceleration rather than specialized neuromorphic hardware. That said, there are natural synergies.
For example, AMD’s Instinct accelerators, designed for high-performance computing and AI, provide a platform for researchers to experiment with neuromorphic algorithms. While not strictly neuromorphic, these accelerators offer the power and flexibility needed to simulate large-scale spiking neural networks.
AMD has also developed software tools that could support neuromorphic computing research. For example, The company’s ROCm (Radeon Open Compute) platform includes libraries for deep learning and scientific computing that can be adapted for neuromorphic simulations.
Nvidia, known mainly for its GPUs, could also easily fit in neuromorphic computing as part of its broader AI strategy. Nvidia’s approach differs from the more specialized neuromorphic chips developed by Intel and IBM. Instead, Nvidia has been incorporating brain-inspired principles into its AI hardware and software stack.
For example, Nvidia has focused on developing hardware accelerators that can efficiently run spiking neural networks (SNNs). These networks are designed to more closely mimic the way biological neurons communicate, using discrete spikes of activity rather than continuous signals. This has allowed researchers to experiment with neuromorphic algorithms on widely available hardware.
In addition to hardware support, Nvidia has also developed software tools to facilitate neuromorphic computing research. The company’s CUDA Deep Neural Network library (cuDNN) has been extended to support sparse and event-driven computations. These types of computations are highly characteristic of neuromorphic systems. As AI continues to evolve, Nvidia will likely play a major role in bridging traditional deep learning approaches with more brain-like computing paradigms.
Qualcomm has been an early adopter of neuromorphic computing principles, with its efforts dating back to the Zeroth project. This initiative, launched in the early 2010s, aimed to develop a brain-inspired computing platform for mobile devices. The Zeroth chip was designed to process sensory data more efficiently by mimicking the human brain’s information processing methods.
Zeroth incorporated artificial neurons and synapses, creating a more brain-like processing architecture. This approach was intended to enable more natural and efficient handling of tasks like image recognition, speech processing, and other forms of sensory data analysis. One of the key goals was to create AI systems that could learn and adapt in real-time, similar to biological brains
The learnings from the Zeroth project have likely influenced Qualcomm’s ongoing AI and machine learning initiatives, particularly in the development of their Neural Processing Units (NPUs) for mobile devices. These NPUs, now integrated into Qualcomm’s mobile system-on-chip (SoC) designs, are designed to handle AI workloads more efficiently than traditional CPU or GPU architectures. While not fully neuromorphic, these NPU designs incorporate brain-inspired principles.
Top Neuromorphic Computing Stocks for 2025: Ranked by Pure-Play Focus
This guide covers the top neuromorphic computing stocks and companies for investors to watch, ranked by their pure-play focus.
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Top Neuromorphic Computing Stocks for 2024: Ranked by Pure-Play Focus
- Last updated: August 6, 2024
The human brain consumes about 20 watts of power, which is roughly equivalent to a dim light bulb. Despite this low power consumption, it can perform an estimated 10^16 operations per second. In contrast, a high-end Nvidia GPU consumes about 400 watts of power to perform a “measly” 10^14 to 10^15 operations per second. This means the human brain is approximately 100,000 to 1,000,000 times more energy-efficient than modern GPUs.
But what if we could replicate that amazing efficiency on a physical computer chip? That’s the ambitious vision for neuromorphic computing. The neuromorphic computing market, while still in its infancy, is projected to grow by 21.2% annually (CAGR) over the next five years. So in this guide, we’ll explore the top neuromorphic computing stocks, ranked by pure-play focus.
The key growth drivers for the neuromorphic computing market stem from several high-impact sectors.. For example, the automotive industry is pushing for more advanced driver assistance systems and fully autonomous vehicles. These require real-time processing of vast amounts of sensory data with minimal latency and power consumption – an ideal use case for neuromorphic chips.
Mobile and edge AI is another huge demand driver. As AI capabilities become more important in smartphones and other mobile devices, neuromorphic chips offer a way to run sophisticated AI models without quickly draining batteries or requiring constant cloud connectivity.
That said, keep in the mind that neuromorphic computing is a bleeding edge technology. Creating chips that truly mimic the brain’s neuron and synapse structure at scale is an enormously complex task, and current neuromorphic chips are highly limited in their neuron count compared to the human brain. Also traditional software development approaches don’t translate directly to neuromorphic systems. This presents a major (and still unsolved) challenge in integration with existing computing ecosystems.
Note: We make every effort to keep our information accurate and up-to-date. However, technology markets do move fast and company situations can change rapidly. Please use this guide as an intro to the neuromorphic computing landscape; but ultimately, do your own due diligence before taking action.
Tier 1: Pure-Play Neuromorphic Computing Stocks
The pure-play neuromorphic computing stocks represent the cutting edge of this nascent industry. These companies stake their entire business model on the potential of these brain-inspired chips. While this focus creates higher risk, it also offers the most direct exposure for investors bullish on neuromorphic computing. With only one public company currently in this tier, it underscores just how early we are in the neuromorphic computing market.BrainChip Holdings (ASX: BRN)
BrainChip Holdings (ASX: BRN) is a first-mover in commercial neuromorphic computing, with a focus on energy-efficient edge AI.Australia-based BrainChip is a pioneer in commercializing neuromorphic computing, focusing on edge AI solutions. The company has developed an Edge AI platform that combines innovative silicon IP, software, and machine learning. This platform includes the Akida neuromorphic processor. Akida is designed to process information in a way that mimics the human brain from a fundamental hardware level. This “imitation” goes beyond the deep neural networks used in today’s AI models.
Brainchip enjoys first-mover advantage in commercial neuromorphic computing. The company’s technology has several unique features, including microwatt power consumption and on-chip learning, while being able to support standard machine learning workflows. In fact, it offers a claimed 5-10x improvement in performance-per-watt over traditional AI accelerators. This would make the Akida chip ideal for battery-powered devices, edge computing, and in-sensor intelligence.
The company is pursuing a flexible business model centered on high-margin IP licensing. This strategy involves upfront license fees and ongoing royalties, which could provide steady revenue as adoption grows. BrainChip’s intellectual property portfolio includes 17 granted patents and 30 pending patents. The company’s team consists of 80% engineers, with 15% holding PhDs from leading AI research programs. BrainChip is also building partnerships with system integrators, including MegaChips, Prophesee, and SiFive.
Tier 2: Tech Giants with Neuromorphic Initiatives
This tier features established tech giants making strategy investments in neuromorphic computing. These companies offer a blend of stability and innovation. The neuromorphic projects from these companies benefit from substantial R&D budgets and existing technological ecosystems. However, neuromorphic computing remains a small part of their overall business. This tier reveals how seriously major players view the potential of brain-inspired chips, even if commercialization is still years out.Intel Corporation (INTC)
Intel (INTC) developed Loihi, a neuromorphic chip with millions of artificial neurons that excels at real-time learning and adaptation.Intel’s Loihi neuromorphic chip represents a significant advancement in brain-inspired computing. The chip contains millions of artificial neurons and synapses, allowing it to process information in a manner similar to biological neural networks. This architecture enables Loihi to excel at tasks that involve learning and adapting to new information in real-time, a capability that traditional computing systems often struggle with.
One of Loihi’s key features is its energy efficiency. The chip can perform certain types of AI computations using up to 1,000 times less energy than conventional processors. This efficiency stems from its event-driven processing, where neurons only activate when they receive sufficient input, similar to how biological neurons function. This approach allows Loihi to handle complex cognitive tasks while consuming minimal power, making it particularly suitable for edge computing and IoT applications.
Loihi’s versatility has been evident in various research applications. Intel has used the chip to solve optimization problems, control robotic systems, and process sensory data. For example, Loihi has been applied to gesture recognition, real-time image classification, and even olfactory processing. The chip’s ability to learn and adapt quickly has also shown promise in creating AI systems that can operate effectively in dynamic, unpredictable environments. As Intel continues to refine and scale up its neuromorphic technology, Loihi could potentially bridge the gap between traditional computing and the complex, adaptive capabilities of biological brains.
IBM (IBM)
IBM (IBM) created TrueNorth, an energy-efficient neuromorphic chip capable of processing billions of synaptic operations per second per watt.IBM’s TrueNorth neuromorphic chip architecture represents a significant leap in brain-inspired computing. The chip consists of a network of neurosynaptic cores, each containing 256 neurons that can establish up to 256 connections with other neurons. This dense, interconnected structure allows TrueNorth to process information in a highly parallel manner, similar to biological neural networks. The architecture is particularly adept at handling sensory data and performing pattern recognition tasks.
TrueNorth’s energy efficiency is one of its standout features. The chip can perform complex cognitive tasks while consuming only a fraction of the power required by traditional processors. This efficiency is achieved through its event-driven design, where neurons only activate and consume energy when they receive sufficient input. As a result, TrueNorth can process billions of synaptic operations per second per watt, making it well-suited for deployment in power-constrained environments such as mobile devices and IoT sensors.
IBM has demonstrated TrueNorth’s capabilities in various real-world applications. The chip has been used for tasks such as real-time object recognition, audio processing, and complex data analysis. For instance, TrueNorth has been applied to analyze satellite imagery for disaster response, detect anomalies in financial transactions, and even assist in medical diagnosis. The chip’s ability to learn and adapt to new patterns makes it particularly useful in scenarios where data streams are constantly changing. As IBM continues to develop and scale its neuromorphic technology, TrueNorth could play a crucial role in advancing AI systems that can operate more efficiently and effectively in complex, real-world environments.
These phase change neurons store states in response to neuronal inputs. Credit: IBM Research
Tier 3: AI Chipmakers with Neuromorphic Research
The final tier consists of AI chip companies exploring neuromorphic principles. These companies provide more indirect exposure to the field. While not fully committed to neuromorphic designs, these companies are incorporating brain-inspired elements into their existing AI hardware. This tier illustrates how the lines between traditional AI acceleration and neuromorphic computing are blurring, potentially leading to hybrid approaches that could accelerate adoption.Advanced Micro Devices (AMD)
Advanced Micro Devices (AMD) leverages its high-performance computing platforms to support neuromorphic computing research.AMD, while primarily known for its CPUs and GPUs, could fit neuromorphic computing into its broader AI strategy. Keep in mind though, AMD’s current approach to neuromorphic computing is not publicized. The company is still focused more on general-purpose AI acceleration rather than specialized neuromorphic hardware. That said, there are natural synergies.
For example, AMD’s Instinct accelerators, designed for high-performance computing and AI, provide a platform for researchers to experiment with neuromorphic algorithms. While not strictly neuromorphic, these accelerators offer the power and flexibility needed to simulate large-scale spiking neural networks.
AMD has also developed software tools that could support neuromorphic computing research. For example, The company’s ROCm (Radeon Open Compute) platform includes libraries for deep learning and scientific computing that can be adapted for neuromorphic simulations.
Nvidia (NVDA)
Nvidia (NVDA) incorporates brain-inspired principles into its AI hardware and software stack, supporting efficient spiking neural networks.Nvidia, known mainly for its GPUs, could also easily fit in neuromorphic computing as part of its broader AI strategy. Nvidia’s approach differs from the more specialized neuromorphic chips developed by Intel and IBM. Instead, Nvidia has been incorporating brain-inspired principles into its AI hardware and software stack.
For example, Nvidia has focused on developing hardware accelerators that can efficiently run spiking neural networks (SNNs). These networks are designed to more closely mimic the way biological neurons communicate, using discrete spikes of activity rather than continuous signals. This has allowed researchers to experiment with neuromorphic algorithms on widely available hardware.
In addition to hardware support, Nvidia has also developed software tools to facilitate neuromorphic computing research. The company’s CUDA Deep Neural Network library (cuDNN) has been extended to support sparse and event-driven computations. These types of computations are highly characteristic of neuromorphic systems. As AI continues to evolve, Nvidia will likely play a major role in bridging traditional deep learning approaches with more brain-like computing paradigms.
Qualcomm (QCOM)
Qualcomm (QCOM) is an early adopter of neuromorphic computing principles, influencing the development of its NPUs for mobile devices.Qualcomm has been an early adopter of neuromorphic computing principles, with its efforts dating back to the Zeroth project. This initiative, launched in the early 2010s, aimed to develop a brain-inspired computing platform for mobile devices. The Zeroth chip was designed to process sensory data more efficiently by mimicking the human brain’s information processing methods.
Zeroth incorporated artificial neurons and synapses, creating a more brain-like processing architecture. This approach was intended to enable more natural and efficient handling of tasks like image recognition, speech processing, and other forms of sensory data analysis. One of the key goals was to create AI systems that could learn and adapt in real-time, similar to biological brains
The learnings from the Zeroth project have likely influenced Qualcomm’s ongoing AI and machine learning initiatives, particularly in the development of their Neural Processing Units (NPUs) for mobile devices. These NPUs, now integrated into Qualcomm’s mobile system-on-chip (SoC) designs, are designed to handle AI workloads more efficiently than traditional CPU or GPU architectures. While not fully neuromorphic, these NPU designs incorporate brain-inspired principles.
Private Neuromorphic Companies to Watch
Neuromorphic computing is an incredibly young field, and many of the most promising startups are still private. In addition to BrainChip (which is already public), we cover five other innovative startups—SynSense, GrAI Matter Lab, Prophesee, Innatera, and MemComputing—on this page.
Fullmoonfever
Top 20
Was just over on the other site for a squiz.
Interesting comment by @Csharmo who I don't think is over here (?) who feels we are working with Wabtec.
Never looked at them myself but did a very quick google and this Dir popped up. Not looked too deeply yet.
Now....is he just a SH or maybe finds some interest in a recent BRN post.fron 3 weeks ago re the Neurobus, Frontgrade, Airbus consortium
www.wabteccorp.com
Interesting comment by @Csharmo who I don't think is over here (?) who feels we are working with Wabtec.
Never looked at them myself but did a very quick google and this Dir popped up. Not looked too deeply yet.
Now....is he just a SH or maybe finds some interest in a recent BRN post.fron 3 weeks ago re the Neurobus, Frontgrade, Airbus consortium
Revolutionizing the Way the World Moves
At the leading edge of driving innovative technologies and responsible operations across the world.Drawing on over 150 years of experience, we are leading the way in safety, efficiency, reliability, innovation, and productivity.
Last edited:
So....all these changes.
New CMO, Peter out of retirement, reconstituting the SAB, shuttering Perth.
Raising >$20m
A direct offering that may have fallen flat.
What does this all mean for the trajectory of BrainChip?? What's next?
For a thread with a multitude of opinions, to my mind, has been very quiet.
New CMO, Peter out of retirement, reconstituting the SAB, shuttering Perth.
Raising >$20m
A direct offering that may have fallen flat.
What does this all mean for the trajectory of BrainChip?? What's next?
For a thread with a multitude of opinions, to my mind, has been very quiet.
Smoothsailing
Regular
What do you mean Peter out of retirement ?. And this direct offering falling flat
We’re is this coming from ?.
Wabtec makes sense. High tech freight and transport business.Was just over on the other site for a squiz.
Interesting comment by @Csharmo who I don't think is over here (?) who feels we are working with Wabtec.
Never looked at them myself but did a very quick google and this Dir popped up. Not looked too deeply yet.
Now....either maybe a SH or maybe finds some interest in a recent BRN post.fron 3 weeks ago re the Neurobus, Frontgrade, Airbus consortium
Revolutionizing the Way the World Moves
At the leading edge of driving innovative technologies and responsible operations across the world.Drawing on over 150 years of experience, we are leading the way in safety, efficiency, reliability, innovation, and productivity.
View attachment 67747 View attachment 67748
They use electric battery power.AKIDA reduces power consumption.
Wabtec are big on safety. AKIDA has OH & S and maintenance detection capabilities which may assist their braking and other systems.
We are thrilled to announce BrainChip as a Gold Partner of AI Hardware & Edge AI Summit 2024! | AI Infra Connect
We are thrilled to announce BrainChip as a Gold Partner of AI Hardware & Edge AI Summit 2024! 🚀 BrainChip leads in edge AI on-chip processing with its Akida™ neuromorphic processor. Mimicking the human brain, Akida enables efficient, high-precision data classification at acquisition...
AI Hardware & Edge AI Summit 2024
The combined AI Hardware & Edge AI Summit comprehensively covers the design and deployment of ML hardware and software infrastructure across the cloud-edge continuum.
Yeah and Mr T&j just right now exposed himself again with one more account “lies&damnlies” not that I was expecting something different, but at least he don’t hide it …
buena suerte :-)
BOB Bank of Brainchip
Morning Chippers
A green Friday maybe ????
I'm very much in need of a HUGE pick me up
ANNOUNCEMENT ...So say all!
Please please please Brainchip.........
A green Friday maybe ????
I'm very much in need of a HUGE pick me up
ANNOUNCEMENT ...So say all!Please please please Brainchip.........
If I were the "Bank of Brainchip," I definitely would make sure everyone of us (of you) got a little payout!Morning Chippers
A green Friday maybe ????
I'm very much in need of a HUGE pick me up
Please please please Brainchip.........
FREE SHARES FOR EVERYONE !!!! But until then, we’ll just have to keep dreaming and keep an eye on the current developments. 
buena suerte :-)
BOB Bank of Brainchip
Yep and now a very depleted..... 'BoB' ...If I were the "Bank of Brainchip," I definitely would make sure everyone of us (of you) got a little payout!
Samsung Electronics Begins Mass Production of Industry’s Thinnest LPDDR5X DRAM Packages for On-Device AI
Samsung’s compact LPDDR5X DRAM packages measure 0.65mm high, allowing enhanced thermal control suitable for on-device AI mobile applications The LPDDR package stacks four layers of 12nm-class DRAM die, reducing thickness and improving heat resistance while increasing density
Come on brainchip… that’s your playfield …
www.businesskorea.co.kr
The Rise of On-Device AI: Faster, More Secure, and Cost-Effective Solutions Needed
The era of On-Device AI has officially begun, marking a significant shift in the landscape of artificial intelligence technology. This new phase is characteriz
Afternoon 7fur7 ,Samsung Electronics Begins Mass Production of Industry’s Thinnest LPDDR5X DRAM Packages for On-Device AI
Samsung’s compact LPDDR5X DRAM packages measure 0.65mm high, allowing enhanced thermal control suitable for on-device AI mobile applications The LPDDR package stacks four layers of 12nm-class DRAM die, reducing thickness and improving heat resistance while increasing densitynews.samsung.com
Interesting..... from the site you have provided above , under products....
Might be one for Diogenese to clarify if we may or may not be incorperated in this partnership between Samsung & MediaTek.
javascript:;
ps://bit.ly/3xZboWV
Samsung Completes Validation of Industry’s Fastest LPDDR5X for Use With MediaTek’s Flagship Mobile Platform
Korea on July 16, 2024Audio AUDIO Play/Stop
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Samsung’s 10.7Gbps LPDDR5X was validated on MediaTek’s next-generation Dimensity platform
With over 25% improvement in power consumption and performance, new DRAM enables longer battery life and more powerful on-device AI features for mobile
Samsung Electronics, the world leader in advanced memory technology, today announced it has successfully completed verification of the industry’s fastest 10.7 gigabit-per-second (Gbps) Low Power Double Data Rate 5X (LPDDR5X) DRAM for use on MediaTek’s next-generation Dimensity platform.
The 10.7Gbps operation speed verification was carried out using Samsung’s LPDDR5X 16-gigabyte (GB) package on MediaTek’s upcoming flagship Dimensity 9400 System on Chip (SoC), scheduled to be released in the second half of this year. The two companies have closely collaborated to complete the verification within just three months.
Samsung’s 10.7Gbps LPDDR5X delivers more than 25% improved power consumption and performance compared to the previous generation. This allows longer battery life for mobile devices and enhanced on-device AI performance, boosting the speed of AI features, such as voice-text generation, without requiring server or cloud access.
“Through our strategic cooperation with MediaTek, Samsung has verified the industry’s fastest LPDDR5X DRAM that is poised to lead the AI smartphone market,” said YongCheol Bae, Executive Vice President of Memory Product Planning at Samsung Electronics. “Samsung will continue to innovate through active collaboration with customers and provide optimum solutions for the on-device AI era.”
“Working together with Samsung Electronics has made it possible for MediaTek’s next-generation Dimensity chipset to become the world’s first to be validated at LPDDR5X operating speeds up to 10.7Gbps, enabling upcoming devices to deliver AI functionality and mobile performance at a level we’ve never seen before,” said JC Hsu, Corporate Senior Vice President at MediaTek. “This updated architecture will make it easier for developers and users to leverage more AI capabilities and take advantage of more features with less impact on battery life.”
Amid the expansion of the on-device AI market, especially for AI smartphones, energy-efficient, high-performance LPDDR DRAM solutions are becoming increasingly important. Through the validation with MediaTek, Samsung is solidifying its technological leadership in the low-power, high-performance DRAM market and is expected to expand the application beyond mobile to servers, PCs and automotive devices.
LPDDR DRAMLPDDR5XMediaTekSamsung DRAM
Regards ,
Esq.
And this from German forum thanks to Ravn!
www.linkedin.com
#aihwedgeaisummit #ai #edgeai #aihardware | AI Infra Connect (formerly AI Hardware & Systems)
We are thrilled to announce BrainChip as a Gold Partner of AI Hardware & Edge AI Summit 2024! 🚀 BrainChip leads in edge AI on-chip processing with its Akida™ neuromorphic processor. Mimicking the human brain, Akida enables efficient, high-precision data classification at acquisition...
Hello! I do come visit here, mainly just read. I try and stay in HC and balance out the downrampersWas just over on the other site for a squiz.
Interesting comment by @Csharmo who I don't think is over here (?) who feels we are working with Wabtec.
Never looked at them myself but did a very quick google and this Dir popped up. Not looked too deeply yet.
Now....is he just a SH or maybe finds some interest in a recent BRN post.fron 3 weeks ago re the Neurobus, Frontgrade, Airbus consortium
Revolutionizing the Way the World Moves
At the leading edge of driving innovative technologies and responsible operations across the world.Drawing on over 150 years of experience, we are leading the way in safety, efficiency, reliability, innovation, and productivity.
View attachment 67747 View attachment 67748
40% better NPU performance in Mediatek's Dimensity 9400 soc.
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