BRN Discussion Ongoing
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Frangipani
Top 20
While the authors of this preprint - published on arXiv yesterday - believe the mixed-signal design approach for neuromorphic systems is the way to go, at least we get a nice mention alongside the usual suspects Loihi, NorthPole, SpiNNaker and BrainScaleS!
On reading the corresponding author’s name, Murat Can Işık, I happened to recall him liking a Brainchip post the other day (although scrolling through his LinkedIn reactions just now to retrieve it somehow reminded me of Rob Telson’s generosity in handing out
)
On reading the corresponding author’s name, Murat Can Işık, I happened to recall him liking a Brainchip post the other day (although scrolling through his LinkedIn reactions just now to retrieve it somehow reminded me of Rob Telson’s generosity in handing out
)
cosors
👀
Exactly that. That's why I really like his humour. Methinks he's never ambiguous.How come? Wasn't he serious?
AND how do you know they are both female? (mares)
And yes, I think I know that you're both female. You yourself said that Bravo is like your sister, for you. So I'll take you at your word. With Frangi - well, I think that's obvious.
And the other comment before was right. I wouldn't never ever mess with Frangi, my girlfriend is enough for me
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Sirod69
bavarian girl ;-)
Krustor and Frangipani had disagreements, I only wanted to settlement the disputeExactly that. That's why I really like his humour. Methinks he's never ambiguous.
And yes, I think I know that you're both female. With you it goes against our TSE rules to name my reasoning and with Frangi - well, I think that's obvious.
And the other comment before was absolutely right. I wouldn't ever mess with Frangi, my girlfriend is enough for me
There is no word for "schlichten" in English.
cosors
👀
That's why you got a heart from me!Krustor and Frangipani had disagreements, I only wanted to settlement the dispute
Good Morning Pmel ,Neuromorphic Computing: Making Space Smart
Neuromorphic Computing: Making Space Smart
Eric Gallo, Sr. Principal, Future Technologies R&D, Accenture Labs Space technology is advancing, and companies that never considered themselves space companies are now relying on satellite data and other space services to operate and grow their business.www.linkedin.com
View attachment 59482
Very nice .
Accenture Labs just got themselves added to the BRAINCHIP SCROLL.
We have in the past done a fireside chit chat , podcast with Accenture , thay are a rather large Co.
. Regards,
Esq.
cosors
👀
This is so good that I have just downloaded it. Thanks, there's a lot to laugh about again today
And today I saw in my company that vent is being pushed in instead of dismantle (?) heat potential. How funny. Everything is thermodynamics.
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AI_Inquirer
Regular
Great find, I know we’ve done podcasts with Accenture previously. This is the first confirmed reference to a project and direct use I’m aware of. Audio processing as well is interesting.Neuromorphic Computing: Making Space Smart
Neuromorphic Computing: Making Space Smart
Eric Gallo, Sr. Principal, Future Technologies R&D, Accenture Labs Space technology is advancing, and companies that never considered themselves space companies are now relying on satellite data and other space services to operate and grow their business.
View attachment 59482
Chippers ,
en.m.wikipedia.org
The SCROLL , now at 61 partners.
Fun Fact.... Accenture ( as a whole ) was ranked No.160 of world's largest companys in 2023.
Oh , and globaly had over 733,000 employees.
.
Regards,
Esq.
Accenture - Wikipedia
The SCROLL , now at 61 partners.
Fun Fact.... Accenture ( as a whole ) was ranked No.160 of world's largest companys in 2023.
Oh , and globaly had over 733,000 employees.
.Regards,
Esq.
Attachments
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cosors
👀
I'm not sure I would want to walk around nakedin front of these internet connected robots, which is where AKIDA would come in handy IMO.
I guess with Jetson the batteries go dry quickly.)
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Chippers ,
Something for one of our better sluthers.
Just perusing old notes and stumbled on this scrap of paper.
Annoyingly I did not write what / or who this submission is from..... it may trigger something in another Chipper who follows NASA contracts.
Awarded shortly apparently.
Regards,
Esq.
Something for one of our better sluthers.
Just perusing old notes and stumbled on this scrap of paper.
Annoyingly I did not write what / or who this submission is from..... it may trigger something in another Chipper who follows NASA contracts.
Awarded shortly apparently.
Regards,
Esq.
Attachments
Earlyrelease
Regular
Chippers.
Below is the NICE 2024 conference purpose and objective for April in USA
Key Outcomes
Value proposition for Neuro-inspired / Neuromorphic Computing Systems:
Why would you use these new systems to solve the hardest problems?
Pathways for building, evaluating and improving new learning, analysis, prediction and control systems.
Conference Goal
To bring together researchers from different scientific disciplines and application areas,
for development of next generation information processing and computation architectures
that go beyond stored program architecture and Moore’s Law limits.
At this conference, we will:
Present applications that are looking for solutions that are beyond the capabilities of current computational systems,
Highlight technical approaches that are at the early to middle stages of development for new computational systems,
Identify pathways and resources to accelerate the development of these new systems.
Attendance: 200-250 from neuroscience, systems, microelectronics, applications, potential funding agencies.
Below is the NICE 2024 conference purpose and objective for April in USA
Key Outcomes
Value proposition for Neuro-inspired / Neuromorphic Computing Systems:
Why would you use these new systems to solve the hardest problems?
Pathways for building, evaluating and improving new learning, analysis, prediction and control systems.
Conference Goal
To bring together researchers from different scientific disciplines and application areas,
for development of next generation information processing and computation architectures
that go beyond stored program architecture and Moore’s Law limits.
At this conference, we will:
Present applications that are looking for solutions that are beyond the capabilities of current computational systems,
Highlight technical approaches that are at the early to middle stages of development for new computational systems,
Identify pathways and resources to accelerate the development of these new systems.
Attendance: 200-250 from neuroscience, systems, microelectronics, applications, potential funding agencies.
Earlyrelease
Regular
Further the Nice tutorials that follow the event.
NICE 2024 Tutorials
The Tutorial Day of NICE 2024 (Friday, 26 April 2024) will take place with three slots of three sessions in parallel each, so there will be 9 tutorials offered.
These 9 tutorials have been selected from the submitted tutorial proposals. Assigning the timing is pending.
Hands-on tutorial: BrainScales neuromorphic compute system
An Introduction to Design and Simulation using SNS-Toolbox and SNSTorch
Simulation Tool for Asynchronous Cortical Streams (STACS)
N2A – neural programming language and workbench
SANA-FE: Simulating Advanced Neuromorphic Architectures for Fast Exploration
An Integrated Toolbox for Creating Neuromorphic Edge Applications
CrossSim: A Hardware/Software Co-Design Tool for Analog In-Memory Computing
Neuromorphic Intermediate Representation
Building Scalable, Composable Spiking Neural Algorithms with Fugu (An Introduction)
NICE 2024 Tutorials
The Tutorial Day of NICE 2024 (Friday, 26 April 2024) will take place with three slots of three sessions in parallel each, so there will be 9 tutorials offered.
These 9 tutorials have been selected from the submitted tutorial proposals. Assigning the timing is pending.
Hands-on tutorial: BrainScales neuromorphic compute system
An Introduction to Design and Simulation using SNS-Toolbox and SNSTorch
Simulation Tool for Asynchronous Cortical Streams (STACS)
N2A – neural programming language and workbench
SANA-FE: Simulating Advanced Neuromorphic Architectures for Fast Exploration
An Integrated Toolbox for Creating Neuromorphic Edge Applications
CrossSim: A Hardware/Software Co-Design Tool for Analog In-Memory Computing
Neuromorphic Intermediate Representation
Building Scalable, Composable Spiking Neural Algorithms with Fugu (An Introduction)
Neuromorphic Computing: Making Space Smart
Neuromorphic Computing: Making Space Smart
Eric Gallo, Sr. Principal, Future Technologies R&D, Accenture Labs Space technology is advancing, and companies that never considered themselves space companies are now relying on satellite data and other space services to operate and grow their business.
View attachment 59482 View attachment 59487
Like by the MD for 26 years at accenture.
Dyor
Edge Impulse Optimizes NVIDIA AI for the Edge
Edge Impulse is pleased to announce that we have unlocked previously inaccessible NVIDIA AI capabilities for any edge device with NVIDIA TAO and Omniverse, alongside our launch of native support for NVIDIA Jetson Orin hardware.
www.edgeimpulse.com
Baron Von Ricta
Regular
Good Morning all,
Today is International Day of Happiness.
I know we all at some point on here suffer from irritation, so maybe for just one day we all just have a happy day together.
Find time to just sit back and have a laugh.
Today is International Day of Happiness.
I know we all at some point on here suffer from irritation, so maybe for just one day we all just have a happy day together.
Find time to just sit back and have a laugh.
Fullmoonfever
Top 20
Great pick up @PmelNeuromorphic Computing: Making Space Smart
Neuromorphic Computing: Making Space Smart
Eric Gallo, Sr. Principal, Future Technologies R&D, Accenture Labs Space technology is advancing, and companies that never considered themselves space companies are now relying on satellite data and other space services to operate and grow their business.
View attachment 59482 View attachment 59487
Like by the MD for 26 years at accenture.
Dyor
Was curious previously if we had any connection with them and which auto client they were close to?
BRN Discussion Ongoing
And can be bad news for us as well. As we work closely with Sifive. Would need to check but I'm not sure SiFive architecture etc is open source. Some of their software, tools, specs etc might be. So, could be ok for them and us if they tied into the US and allies plus have the difference of...
thestockexchange.com.au
Wondering how close we are with Accenture as well.
Paper came up as Sept 23 on Google but the copyright is 2021.
What I found interesting was this little bit...wonder who the auto client was and whose neuromorphic they were playing with...Intel, us?
Timeline may have fit with EQXX...maybe?
Paper:
HERE
Then we had the podcast with Accenture earlier this year and our recent Gen 2 Ann has quotes as below from who...same guy now at Verax.
“Generative AI and LLMs at the Edge are key to intelligent situational awareness in verticals from manufacturing to healthcare to defense,” said Jean-Luc Chatelain, MD of Verax Capital Advisors and former MD and Global CTO at Accenture Applied Intelligence. “Disruptive innovation like BrainChip TENNs support Vision Transformers built on the foundation of neuromorphic principles, can deliver compelling solutions in ultra-low power, small form factor devices at the Edge, without compromising accuracy.”
Bravo
If ARM was an arm, BRN would be its biceps💪!
NVIDIA Is Being Pushed to the Edge
A collaboration between NVIDIA and Edge Impulse is increasing the accuracy of edge ML models deployed on resource-constrained devices.
https://www.hackster.io/nickbildNick BildFollow
10 hours ago • Machine Learning & AI
Computer vision on edge hardware is maturing rapidly (
: Edge Impulse)As NVIDIA’s GTC 2024 continues this week, the big announcements keep rolling in. We recently reported on the new Blackwell architecture, which enables more practical training and inferencing of massive trillion-parameter machine learning (ML) models. But we would be remiss if we did not also mention the updates at the other end of the spectrum — edge ML. A sledgehammer is not the right tool for every task, after all.
Edge ML allows us to run models directly on devices at the network's edge, such as smartphones, sensors, IoT devices, and other embedded systems. This approach enables data processing and analysis to occur locally on the device itself, rather than relying on centralized servers or cloud infrastructure. The significance of edge ML lies in its ability to address the limitations of traditional techniques that heavily rely on large, remote clusters of powerful computers and GPUs.
These edge techniques enhance privacy and security by keeping sensitive data localized and reducing the risks associated with transmitting data over networks. With the increasing concerns surrounding data privacy and regulations like GDPR and CCPA, organizations are increasingly being compelled to prioritize data protection. Edge ML is also particularly crucial in applications where instantaneous responses are necessary, such as autonomous vehicles, industrial automation, and healthcare monitoring systems. In scenarios like these, even milliseconds of delay can have significant consequences, making edge inferencing indispensable for achieving acceptable performance.
NVIDIA TAO models are now available in Edge Impulse (
: Edge Impulse)At the conference, a collaboration between NVIDIA and Edge Impulse was announced that promises to help edge ML applications mature. The Edge Impulse platform is tailored to building and deploying machine learning models to edge devices. And with their newly released integration with NVIDIA’s TAO and Omniverse, those models will be more accurate and efficient than ever, greatly expanding the number of use cases for edge ML.
Using the NVIDIA TAO Toolkit, developers can create powerful, customized, production-ready computer vision applications. In the past, these applications would need to be deployed to expensive, energy-hungry computing equipment that is not well-suited for portable applications where privacy and speed are required. But now, models trained with TAO can be fine-tuned and deployed using the Edge Impulse platform. And with the optimization tools that are available, these models can be deployed to the tiniest of platforms — even those powered by Arm Cortex-based microcontrollers.
Of course an ML algorithm is only as good as the data it was trained on, so Edge Impulse has also integrated NVIDIA’s Omniverse into their workflow. Omniverse allows organizations to quickly generate large amounts of high-quality synthetic image data. This is especially important where obtaining real-world data is costly, time-consuming, or creates privacy concerns. As data collection can be a major drain on resources, this new feature promises to greatly accelerate time to market for production models.
Taken together, these enhancements will allow users to rapidly create professional-grade industrial ML models that can run on heavily resource-constrained devices. That will open up a new world of possibilities for edge ML, from the visual inspection of manufacturing production lines to detect defects and equipment malfunctions, to surgery inventory object detection to prevent postoperative complications.
machine learning
artificial intelligence
tinyml
energy efficiency
Nick BildFollow
R&D, creativity, and building the next big thing you never knew you wanted are my specialties.
https://embeddedvisionsummit.com/?u..._medium=newsletter&utm_campaign=Hackster+2024
NVIDIA Is Being Pushed to the Edge
A collaboration between NVIDIA and Edge Impulse is increasing the accuracy of edge ML models deployed on resource-constrained devices.
www.hackster.io
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