Tothemoon24
Regular
Edge lmpulse dont muck around
All aboard
All aboard
BRN Discussion Ongoing
- Thread starter TechGirl
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Worth looking at the Arteris and SiFive's recent partnership again.
The SiFive X280 with Akida tagging along looks like it will be joining Arteris to feed some of their hungry customers.
Who are these customers again (Samsung is one):
Customers - Arteris
Arteris, SiFive team for RISC-V edge AI reference design
Arteris has signed a partnership deal with SiFive to help speed up the development of industrial and consumer RISC-V chips for edge AI.
www.eenewseurope.com
Tothemoon24
Regular
MARKETS
CNBC TV
WATCHLIST
Qualcomm CEO
‘ For you to make that happen you can’t run everything in a data center, you’re going to have to bring the Al to the devices “
TECH
The popularity of ChatGPT is a ‘milestone’ in establishing Qualcomm as an A.I. company, CEO says
PUBLISHED WED, MAR 1 20239:27 AM ESTUPDATED WED, MAR 1 2023AT 10:51 EST
Jenni Reid
WATCH LIVE
KEY POINTS
- The chief executive of chipmaker Qualcomm sees the prominence of ChatGPT as a key opportunity to popularize artificial intelligence tools on smartphones.
- “This is the milestone we’ve been waiting for to establish Qualcomm as an AI company,” Cristiano Amon told CNBC.
- Amon said that his company had an edge in creating the required processing power to run large language models without compromising battery life.
WATCH NOW
VIDEO02:32
ChatGPT a ‘milestone’ for Qualcomm as it showcases A.I. smartphone capability, CEO says
The explosive popularity of ChatGPT is an opportunity to show off the capabilities of artificial intelligence on smartphones, according to chip company Qualcomm’s chief executive.
“This is the milestone we’ve been waiting for to establish Qualcomm as an AI company,” Cristiano Amon told CNBC at the Mobile World Congress in Barcelona.
Developed by research company OpenAI, chatbot ChatGPT has been shared widely online, as users ask it to answer questions, generate text or provide detailed, responsive information.
Qualcomm recently released videos of text being used to generate AI images on an Android phone, which it also demonstrated at the conference.
“You want to generate any image that you want to share with somebody, you want to do it in real-time — think about what Microsoft is doing with search, and you want to chat with the search results,” Amon told CNBC’s Karen Tso and Arjun Kharpal. “For you to make that happen, you can’t run everything in a data center, you’re going to have to bring the AI to the devices.”
Large-language models will be generated entirely within smartphones, he said, meaning that they will be able to work without being connected to the internet.
“The ability to create that much processing power in a smartphone and run that without compromising the battery life is something that only Qualcomm can do,” he claimed.
WATCH NOW
VIDEO05:01
A.I. race expected to bring flurry of M&A: Trachet CEO
In a note this week, analysts at Bernstein saidthat the powering of AI queries could be a multi-billion dollar annual market opportunity for chipmakers.
Qualcomm has also supplied chips for a variety of virtual reality devices, partnering with the likes of Meta, Samsung and Google.
Amon said that he believed smart glasses were the next frontier of computing and the “merging of physical and digital spaces.”
“I can see a scenario that you’re going to have your companion glasses to your phone, and eventually you’re just going to have the glasses. And the potential is incredible.”
He added, “It’s going to happen, it’s coming very soon.”
Amon also told CNBC that Qualcomm did not expect to produce modems for Apple’s new iPhone in 2024, suggesting that the tech giant’s highly-anticipated move into in-house products may be approaching.
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HopalongPetrovski
I'm Spartacus!
Akida 2nd Generation Platform Brief
The 2nd generation Akida builds on the existing technology foundation and supercharges the processing of raw time-continuous streaming data, such as video analytics, target tracking, audio classification, analysis of health monitoring data such as heart rate and respiratory rate for vital signs prediction, and time series analytics used in forecasting, and predictive production line maintenance. These capabilities are critically needed in industrial, automotive, digital health, smart home, and smart city applications.
Download Platform Brief
Availability:
Engaging with lead adopters now. General availability in Q3’2023
WOW! This truly is Science fiction indeed! Roll over Darling and back to sleep. I have reading to do!
HopalongPetrovski
I'm Spartacus!
One IP Platform, Multiple Configurable Products
The 2nd generation IP platform will support a very wide range of market verticals and will be delivered in three classes of product.
Akida-E: Extremely energy-efficient, for always on operation very close to, or at sensors.
Akida-S: Integration into MCU or other general purpose platforms that are used in broad variety of sensor-related applications
Akida-P: Mid-range to higher end configurations with optional vision transformers for ground breaking and yet efficient performance.
Contact Sales
And our synapse is back! All those with tattoo's will be very happy! Love it!
Tothemoon24
Regular
HopalongPetrovski
I'm Spartacus!
However, traditional computer vision techniques like Convolutional Neural Networks (CNNs) have some limitations when analyzing images.
CNNs work by passing images through a series of convolutional and pooling layers to extract relevant features.
However, as images become larger and more complex, CNNs become less effective.
This is where Vision Transformers come in.
Vision Transformers, or ViTs, are a type of deep learning model that uses self-attention mechanisms to process visual data.
They were introduced in a paper by Dosovitskiy in 2020 and have since gained popularity in computer vision research.
In a Vision Transformer, an image is first divided into patches, which are then flattened and fed into a multi-layer transformer network.
The self-attention mechanism allows the model to attend to different parts of the image at different scales, enabling it to simultaneously capture global and local features.
The transformer’s output is passed through a final classification layer to obtain the predicted class label.
The result is a Vision Transformer with self-attention mechanisms that allow the model to focus on different parts of the input image in the spatial dimension.
ViTs show higher accuracy with object classification tasks compared to CNNs.
Due to self-attention used in ViTs they are able to learn features from spatially related regions and becomes very suitable for object detection, tracking and segmentation related solutions. They also have better generalization when trained on a distributed dataset.
This is because transformers utilize multiheaded attention, which is a novel way to extract meaning from sequences of words.
When considering vision transformers, the input to the network is no longer a sequence of words, but frames that have meaning within the images, and across several images that arrive in the form of a video. Vision transformers use multiheaded attention to pay attention to specific things inside of an image.
Enabled by Akida, Vision Transformers can be applied remotely at low power consumption to serve multiple industries.
CNNs work by passing images through a series of convolutional and pooling layers to extract relevant features.
However, as images become larger and more complex, CNNs become less effective.
This is where Vision Transformers come in.
Vision Transformers, or ViTs, are a type of deep learning model that uses self-attention mechanisms to process visual data.
They were introduced in a paper by Dosovitskiy in 2020 and have since gained popularity in computer vision research.
In a Vision Transformer, an image is first divided into patches, which are then flattened and fed into a multi-layer transformer network.
The self-attention mechanism allows the model to attend to different parts of the image at different scales, enabling it to simultaneously capture global and local features.
The transformer’s output is passed through a final classification layer to obtain the predicted class label.
The result is a Vision Transformer with self-attention mechanisms that allow the model to focus on different parts of the input image in the spatial dimension.
ViTs show higher accuracy with object classification tasks compared to CNNs.
Due to self-attention used in ViTs they are able to learn features from spatially related regions and becomes very suitable for object detection, tracking and segmentation related solutions. They also have better generalization when trained on a distributed dataset.
This is because transformers utilize multiheaded attention, which is a novel way to extract meaning from sequences of words.
When considering vision transformers, the input to the network is no longer a sequence of words, but frames that have meaning within the images, and across several images that arrive in the form of a video. Vision transformers use multiheaded attention to pay attention to specific things inside of an image.
Vision Transformers Are Popular For Tasks Like
Enabled by Akida, Vision Transformers can be applied remotely at low power consumption to serve multiple industries.
- In the field of autonomous vehicles, they can be used for driver assistance and monitoring, alertness, lane assistance.
- In agriculture, they can be used to monitor crop health, identify diseases or pests, and estimate yield.
- In healthcare, they can help analyze medical images to aid diagnosis.
- In manufacturing, they can automate quality control processes.
- In smart devices, they can be used for user attention, pose estimation, scene understanding, etc.
HopalongPetrovski
I'm Spartacus!
HopalongPetrovski
I'm Spartacus!
Temporal Event-Based Neural Networks – A New Approach To 3D And Time Series Data
As we step into a new era of Al-assisted human intelligence, we are confronted with several technological obstacles, particularly in the areas of:
These challenges must be surmounted to achieve further advancements in this field.
This brings us to the question:
What comes next for loT devices that are optimized for performance on the Edge?
And how will we integrate Al into our everyday devices?
For orders of magnitude less memory, TENNs can achieve excellent performance on tasks that use temporal and spatial information.
Currently, TENNs have already been demonstrated on spatial-temporal data such as:
TENNs process many types of data including one-dimensional time series from
…and many others.
What are the best uses of TENNs?
Akida’s high processing at ultra-low power consumption provides the perfect ground for TENN applications in loT devicesleading to a new generation of Smart Hardware.
When applied to sensors that mimic the five human senses
TENNs can become an extension of our analytical decision-making abilities for real-time processing.
Akida’s high processing at ultra-low power consumption provides the perfect landscape for TENNs applications in AloT
MadMayHam | 合氣道
Regular
Interesting write up
www.forbes.com
I don't think I've ever seen this map before.
Brainchip Extends AI, Machine Learning In Space And Time With Bio-Inspired Neural Networks
Brainchip has introduced a new generation of its unique, bio-inspired Akida line of licensable, configurable neural processing IP.
www.forbes.com
I don't think I've ever seen this map before.
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HopalongPetrovski
I'm Spartacus!
Fembots anyone? 
Seriously, have we just taken a huge step into the future?
Domination at the edge and a step back towards the servers who are now targets themselves?
Well played Brainchip.
It's seemed to be a long time coming, but here you are with the GOODS!
Seriously, have we just taken a huge step into the future?
Domination at the edge and a step back towards the servers who are now targets themselves?
Well played Brainchip.
It's seemed to be a long time coming, but here you are with the GOODS!
thelittleshort
Top Bloke
OMG, see the quotes from partners page.
Akida Generations
Explore cutting-edge technology, advanced neural networks, and unparalleled performance. Unleash the power of akida™ NOW!brainchip.com
thelittleshort
Top Bloke
I think Nandan has included a new partner in his tweet that is not in his LinkedIn post - Ipsolon Research, Inc
DingoBorat
Slim
Even China's picked the story up..
www.chinatimes.com
Translation of text.
"Australian artificial intelligence start-up BrainChip launched the second-generation Akida chip earlier than expected, which spurred the stock price to soar on the 6th, the largest single-day increase this year. BrainChip shares opened higher and rose 16.67% to 0.6 Australian dollars. The company said in February that Akida was still in development and would not be available until later this year"
BrainChip推出第二代Akida晶片 激勵股價狂飆
澳洲人工智慧新創公司BrainChip較預期提前推出第二代Akida晶片，激勵6日股價狂飆，創下今年來單日最大漲幅。BrainChip股價開高走高，終場躥揚16.67％，報0.6澳元。該公司2月表示Akida仍在研發階段，要到今年稍晚才會上市。
Translation of text.
"Australian artificial intelligence start-up BrainChip launched the second-generation Akida chip earlier than expected, which spurred the stock price to soar on the 6th, the largest single-day increase this year. BrainChip shares opened higher and rose 16.67% to 0.6 Australian dollars. The company said in February that Akida was still in development and would not be available until later this year"
HopalongPetrovski
I'm Spartacus!
Looks like a really well crafted marketing release. Suitable and beautiful sci fi images along with tempting tidbits of narrative.
And backed up with confirmation articles like the Forbes piece's and comments from our partners and eco system collaborators.
So glad to be hip deep in Brainchip already.
Keep bringing it baby.
And backed up with confirmation articles like the Forbes piece's and comments from our partners and eco system collaborators.
So glad to be hip deep in Brainchip already.
Keep bringing it baby.
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HopalongPetrovski
I'm Spartacus!
They are the fifth in the scrolling eco system sequence of quotations at the top of the release.
Everyone will want to be associated with us now!
AKIDA BALLISTA.
AKIDA'S EVERYWHERE!
Ipsolon research has a SBIR phase II contract with the US Navy…all about machine learning that use neural networks…
See what partners, customers and industry influencers are saying about the second generation of Akida - Watch the quotes as they scroll - https://lnkd.in/g3UK3nBp #AIoT #AI #Akida #neuromorphic
www.linkedin.com
BrainChip on LinkedIn: #aiot #ai #akida #neuromorphic
See what partners, customers and industry influencers are saying about the second generation of Akida - Watch the quotes as they scroll -…
SERA2g
Founding Member
Some reveals under the “see what they’re saying” section of the 2nd generation product listing.
Such a cool and well put together release.
Hopefully one day our new releases end up being a Steve Jobs style presentation to the masses. Peter up on the stage in a turtle neck sweater, “We are proud to introduce our 4th product, Akida General Intelligence. Let the 4th revolution begin.”
Let’s hope there’s a bit of hype on the market this week. It’d be nice to see the base line shifted significantly higher.
Cheers
Such a cool and well put together release.
Hopefully one day our new releases end up being a Steve Jobs style presentation to the masses. Peter up on the stage in a turtle neck sweater, “We are proud to introduce our 4th product, Akida General Intelligence. Let the 4th revolution begin.”
Let’s hope there’s a bit of hype on the market this week. It’d be nice to see the base line shifted significantly higher.
Cheers
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