BRN Discussion Ongoing
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Pom down under
Top 20
Think this might have been updated regarding the university projects megachips are working with and investigating
Contribution to Society | MegaChips Corporation
www.megachips.co.jp
Dallas
Regular
Open Neuromorphic on LinkedIn: #onm #onm #neurodecode #mindmeld #onminsights
Hi there, #ONM thinkers! Here’s your #ONM News roundup: ▫ Neuromorphic Computing Market to Surge at 89.7% CAGR, Reaching USD 1,325.2 Million by 2030 ▫ What…
Qualcomm at NeurIPS 2024: Our groundbreaking innovations and cutting-edge advancements in AI | Qualcomm
2024 is notable for the remarkable advancements in generative artificial intelligence (GenAI), and Qualcomm Technologies is at the forefront of bringing these capabilities to edge devices. Our Qualcomm AI Research team is dedicated to pushing the boundaries of AI/ML and translating these...
Jilei Hou on LinkedIn: Qualcomm at NeurIPS 2024: Our groundbreaking innovations and cutting-edge…
https://lnkd.in/gMiQzbdg Qualcomm AI Research proudly presents 11 full papers and 5 technology demos (a quarter of the total EXPO demso) at NeurIPS'24 next…
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Boab
I wish I could paint like Vincent
C U Next Tuesday
Geez, i had organised a ceasefire in Ukraine and the Middle East but cancelled it in favour of waiting for the Podcast Eppy 7.
Bravo
If ARM was an arm, BRN would be its biceps💪!
``Frontiers in Neuroscience'' is generally considered a reputable journal in the field of neuroscience. It is part of the Frontiers journal series, which is known for its open-access model and rigorous peer review process.
Manuscript Submission Deadline 21 June 2025
Guidelines
Neuromorphic computing represents a paradigm shift in artificial intelligence, directly inspired by the operational mechanisms of the human brain. Designed to replicate the brain's high efficiency, neuromorphic systems utilize event-driven, spike-based protocols, vastly reducing energy consumption while enabling real-time data processing. Central to these systems are spiking neural networks (SNNs), alongside specialized hardware like field-programmable gate arrays (FPGAs), memristors, and application-specific integrated circuits (ASICs). This approach proves especially valuable for edge computing devices—such as smartphones, drones, and IoT systems—where the demand for quick adaptive responses, low power usage, and efficient learning processes are paramount. The transformative potential of neuromorphic computing extends across numerous sectors including robotics and environmental monitoring, suggesting a future where AI is both more sustainable and ubiquitously adaptable.
The goal of this research topic is to address and overcome the shortcomings of traditional computational architectures that falter in providing energy-efficient and adaptive solutions capable of real-time processing, particularly within edge computing environments. The growing requirements for intelligent automation in areas ranging from IoT to autonomous robotics necessitate a departure from conventional resource-heavy computing methods. Neuromorphic computing emerges as a
formidable contender
in this space, leveraging its bio-inspired designs to facilitate power-conserving, event-driven operations through advanced SNNs and hardware solutions.
In pursuit of deeper insights and technological advancements, this topic focuses on both the progression of neuromorphic hardware and the evolution of SNN algorithms, which are crucial for fine-tuning adaptive learning in practical scenarios. Interdisciplinary collaboration is encouraged to uncover viable strategies for implementing neuromorphic systems across diverse fields, thereby setting the stage for groundbreaking AI innovations that offer low-power, highly adaptable computing alternatives. Specific areas of interest for submission include:
o Neuromorphic and Cognitive Computing Hardware: Investigations into new materials, devices, and structural designs that optimize neuromorphic processing.
o SNN and Cognitive Algorithms: Development of new models and training techniques that enhance capabilities for real-time, adaptive learning.
o Applications in Edge, IoT, Robotics and Cognitive Computing: Case studies that examine the deployment and impact of neuromorphic computing within mobile, robotics and sensor-based platforms.
o Integrative Cross-Disciplinary Research: Studies that merge neuromorphic computing with other domains like robotics, environmental monitoring, or cognitive sciences.
We invite the submission of original research, comprehensive reviews, and insightful case studies that push the boundaries of neuromorphic and cognitive computing, particularly in environments that are limited by resources and require robust, edge-oriented solutions.
www.frontiersin.org
Advancing Adaptive and Energy-Efficient Neuromorphic Computing for Real-Time Edge AI and Robotics
About this Research Topic
Manuscript Summary Submission Deadline 03 March 2025Manuscript Submission Deadline 21 June 2025
Guidelines
Neuromorphic computing represents a paradigm shift in artificial intelligence, directly inspired by the operational mechanisms of the human brain. Designed to replicate the brain's high efficiency, neuromorphic systems utilize event-driven, spike-based protocols, vastly reducing energy consumption while enabling real-time data processing. Central to these systems are spiking neural networks (SNNs), alongside specialized hardware like field-programmable gate arrays (FPGAs), memristors, and application-specific integrated circuits (ASICs). This approach proves especially valuable for edge computing devices—such as smartphones, drones, and IoT systems—where the demand for quick adaptive responses, low power usage, and efficient learning processes are paramount. The transformative potential of neuromorphic computing extends across numerous sectors including robotics and environmental monitoring, suggesting a future where AI is both more sustainable and ubiquitously adaptable.
The goal of this research topic is to address and overcome the shortcomings of traditional computational architectures that falter in providing energy-efficient and adaptive solutions capable of real-time processing, particularly within edge computing environments. The growing requirements for intelligent automation in areas ranging from IoT to autonomous robotics necessitate a departure from conventional resource-heavy computing methods. Neuromorphic computing emerges as a
In pursuit of deeper insights and technological advancements, this topic focuses on both the progression of neuromorphic hardware and the evolution of SNN algorithms, which are crucial for fine-tuning adaptive learning in practical scenarios. Interdisciplinary collaboration is encouraged to uncover viable strategies for implementing neuromorphic systems across diverse fields, thereby setting the stage for groundbreaking AI innovations that offer low-power, highly adaptable computing alternatives. Specific areas of interest for submission include:
o Neuromorphic and Cognitive Computing Hardware: Investigations into new materials, devices, and structural designs that optimize neuromorphic processing.
o SNN and Cognitive Algorithms: Development of new models and training techniques that enhance capabilities for real-time, adaptive learning.
o Applications in Edge, IoT, Robotics and Cognitive Computing: Case studies that examine the deployment and impact of neuromorphic computing within mobile, robotics and sensor-based platforms.
o Integrative Cross-Disciplinary Research: Studies that merge neuromorphic computing with other domains like robotics, environmental monitoring, or cognitive sciences.
We invite the submission of original research, comprehensive reviews, and insightful case studies that push the boundaries of neuromorphic and cognitive computing, particularly in environments that are limited by resources and require robust, edge-oriented solutions.
Advancing Adaptive and Energy-Efficient Neuromorphic Computing for Real-Time Edge AI and Robotics
Neuromorphic computing represents a paradigm shift in artificial intelligence, directly inspired by the operational mechanisms of the human brain. Designed to replicate the brain's high efficiency, neuromorphic systems utilize event-driven, spike-based protocols, vastly reducing energy...
7für7
Top 20
You know, I’m so fed up with this shit stock. I put all my valuables into it, thinking the value would go up, and now my wife is pressuring me to sell everything! Honestly, if I’d known it would end up like this, I would’ve looked for an external storage solution instead. Oh, sorry, I’m talking about our storage closet – that’s where I keep my old valuables. My wife goes on a cleaning spree every Saturday.
7für7
Top 20
I find it amusing how some people in the comments are actually asking whether they’re still working with Akida… What do they expect? That he’ll say, “Yes, we’re working with Akida, and we’ll be using Akida in all our cars”? Let’s be honest, that question will remain unanswered until there’s an official announcement… if there even is one.
Markus Schäfer on LinkedIn: Neuromorphic Computing
Behind door 2 of our 𝐚𝐝𝐯𝐞𝐧𝐭 𝐜𝐚𝐥𝐞𝐧𝐝𝐚𝐫 𝐨𝐟 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 is a topic that could unlock many future functionalities…www.linkedin.com
Yes Markus…… ! Ready when you are bud !
View attachment 73838
7für7
Top 20
@Pom down under looks like the xrp rise continues after a small correction… 
Edit
Maybe I was too quick to feel excited.
Edit
Maybe I was too quick to feel excited.
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7für7
Top 20
The question is what the person expected from the box and what prior knowledge they have. Are their own skills limited? Or did they even think they could just plug in the box, and everything would run automatically? I trust the statements of professionals from official sources within the industry.
This should reinforce NASA's Akida awareness.
So the single order from this one guy is all we need to get the company back ok track?
Didn't realise when Sean said revenue is imminent he meant this one single order.
Great! Grab some mentos on your way out.
CHIPS
Regular
Where is the link please?
Frangipani
Regular
Hi CHIPS,
it’s the same link I had already posted at 4:25 am (AEDT):
Swetha Varadarajulu on LinkedIn: #icssc2024 #5gntn #brainchipakida | 13 comments
🌟 Exciting (but slightly late) news to share! 🌟 I’m thrilled to announce that my paper titled “Enhanced Demodulator for 5G NTN using Spatio-Temporal… | 13 comments on LinkedIn
Schönes Wochenende
Frangipani
Pom down under
Top 20
I read his comments and was quite shocked, you can find then under the short discussion
Pom down under
Top 20
His review was quite shocking, they could of at least supplied him with an Australian plug lol
Townyj
Ermahgerd
That's something you query when buying an item like this. Most of the time they have a drop down box where you select a plug. Since it doesn't have this option you would query before purchasing right..??
These are like $5 each, I am sure the poster would have plenty of them hanging around as well. Considering his so called previous profession.
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