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Good Morning Chippers ,
Regarding the AKIDA PICO.... Amazing .
The AKIDA PICO takes up roughly 0.18 mm squared on a silicone chip in 22nm size format , WOW.
To put this in some perspective....
The stinger of the average Worker
is approximately 2.5mm long.
* Note, Bee Sting stat taken from , NIH , National Library of Medicine.
Regards,
Esq.
Regarding the AKIDA PICO.... Amazing .
The AKIDA PICO takes up roughly 0.18 mm squared on a silicone chip in 22nm size format , WOW.
To put this in some perspective....
The stinger of the average Worker
is approximately 2.5mm long.* Note, Bee Sting stat taken from , NIH , National Library of Medicine.
Regards,
Esq.
buena suerte :-)
BOB Bank of Brainchip
Very nice ....We are getting closer and closer to the much anticipated ....' Power announcement'....BrainChip Introduces Lowest-Power AI Acceleration Co-Processor
Laguna Hills, Calif. – October 1, 2024 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event- based, brain-inspired AI, today introduced the Akida™ Pico, the lowest power acceleration co-processor that enables the creation of very compact, ultra-low power, portable and intelligent devices for wearable and sensor integrated AI into consumer, healthcare, IoT, defense and wake-up applications.
Akida Pico accelerates limited use case-specific neural network models to create an ultra-energy efficient, purely digital architecture. Akida Pico enables secure personalization for applications including voice wake detection, keyword spotting, speech noise reduction, audio enhancement, presence detection, personal voice assistant, automatic doorbell, wearable AI, appliance voice interfaces and more.
The latest innovation from BrainChip is built on the Akida2 event-based computing platform configuration engine, which can execute with power suitable for battery-powered operation of less than a single milliwatt. Akida Pico provides power-efficient footprint for waking up microcontrollers or larger system processors, with a neural network to filter out false alarms to preserve power consumption until an event is detected. It is ideally suited for sensor hubs or systems that need to be monitored continuously using only battery power with occasional need for additional processing from a host.
BrainChip’s exclusive MetaTF™ software flow enables developers to compile and optimize their specific Temporal-Enabled Neural Networks (TENNs) on the Akida Pico. With MetaTF’s support for models created with TensorFlow/Keras and Pytorch, users avoid needing to learn a new machine language framework while rapidly developing and deploying AI applications for the Edge.
Among the benefits of Akida Pico are:
– Ultra-low power standalone NPU core (<1mW)
– Support power islands for minimal standby power
– Industry-standard development environment
– Very Small logic die area
– Optimize overall die size with configurable data buffer and model parameter memory
“Like all of our Edge AI enablement platforms, Akida Pico was developed to further push the limits of AI on-chip compute with low latency and low power required of neural applications,” said Sean Hehir, CEO at BrainChip. “Whether you have limited AI expertise or are an expert at developing AI models and applications, Akida Pico and the Akida Development Platform provides users with the ability to create, train and test the most power and memory efficient temporal-event based neural networks quicker and more reliably.”
BrainChip’s Akida is an event-based compute platform ideal for early detection, low-latency solutions without massive compute resources for robotics, drones, automotive and traditional sense-detect-classify-track solutions. BrainChip provides a range of software, hardware and IP products that can be integrated into existing and future designs, with a roadmap for customers to deploy multi-modal AI models at the edge.
BrainChip Introduces Lowest-Power AI Acceleration Co-Processor
BrainChip Introduces Lowest-Power AI Acceleration Co-Processor Laguna Hills, Calif. – October 1, 2024 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event- based, brain-inspired AI, today introduced the...brainchip.com
.....Thanks Slade
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Bravo
If ARM was an arm, BRN would be its biceps💪!
AN EXTRACT FROM THE ARTICLE
"They showed off their techniques with an application that detects keywords in speech. This is useful for voice assistance like Amazon’s Alexa, which waits for the ‘Hello, Alexa’ keywords to activate."
Brain-like Computers Tackle the Extreme Edge
Start-up BrainChip announces a new chip design for a milliwatt-level AI inference
Dina Genkina6 hours ago
3 min read
Dina Genkina is the computing and hardware editor at IEEE Spectrum
BrainChip's Akida Pico could be used with their AI model architecture in devices like smartwatches.
BrainChip
neuromorphic computing artificial intelligence edge computing ai models
Neuromorphic computing draws inspiration from the brain, and Steven Brightfield, chief marketing officer for Sydney-based startup BrainChip, says that makes it perfect for use in battery-powered devices doing AI processing.
“The reason for that is evolution,” Brightfield says. “Our brain had a power budget.” Similarly, the market BrainChip is targeting is power constrained. ”You have a battery and there’s only so much energy coming out of the battery that can power the AI that you’re using.”
Today, BrainChip announced their chip design, the Akida Pico, is now available. Akida Pico, which was developed for use in power-constrained devices, is a stripped-down, miniaturized version of BrainChip’s Akida design, introduced last year. Akida Pico consumes 1 milliwatt of power, or even less depending on the application. The chip design targets the extreme edge, which is comprised of small user devices such as mobile phones, wearables, and smart appliances that typically have severe limitations on power and wireless communications capacities. Akida Pico joins similar neuromorphic devices on the market designed for the edge, such as Innatera’s T1 chip, announced earlier this year, and SynSense’s Xylo, announced in July 2023.
Neuron Spikes Save Energy
Neuromorphic computing devices mimic the spiking nature of the brain. Instead of traditional logic gates, computational units—referred to as ‘neurons’—send out electrical pulses, called spikes,to communicate with each other. If a spike reaches a certain threshold when it hits another neuron, that one is activated in turn. Different neurons can create spikes independent of a global clock, resulting in highly parallel operation.A particular strength of this approach is that power is only consumed when there are spikes. In a regular deep learning model, each artificial neuron simply performs an operation on its inputs: It has no internal state. In a spiking neural network architecture, in addition to processing inputs, a neuron has an internal state. This means the output can depend not only on the current inputs, but on the history of past inputs, says Mike Davies, director of the neuromorphic computing lab at Intel. These neurons can choose not to output anything if, for example, the input hasn’t changed sufficiently from previous inputs, thus saving energy.
“Where neuromorphic really excels is in processing signal streams when you can’t afford to wait to collect the whole stream of data and then process it in a delayed, batched manner. It’s suited for a streaming, real-time mode of operation,” Davies says. Davies’ team recently published a result showing their Loihi chip’s energy use was one-thousandth of a GPU’s use for streaming use cases.
Akida Pico includes its neural processing engine, along with event processing and model weight storage SRAM units, direct memory units for spike conversion and configuration, and optional peripherals. Brightfield says in some devices, such as simple detectors, the chip can be used as a stand-alone device, without a microcontroller or any other external processing. For other use cases that require further on-device processing, it can be combined with a microcontroller, CPU, or any other processing unit.
BrainChip’s Akida Pico design includes a miniaturized version of their neuromorphic processing engine, suitable for small, battery-operated devices.BrainChip
BrainChip has also worked to develop AI model architectures that are optimized for minimal power use in their device. They showed off their techniques with an application that detects keywords in speech. This is useful for voice assistance like Amazon’s Alexa, which waits for the ‘Hello, Alexa’ keywords to activate.
The BrainChip team used their recently developed model architecture to reduce power use to one-fifth of the power consumed by traditional models running on a conventional microprocessor, as demonstrated in their simulator.
“I think Amazon spends $200 million a year in cloud computing services to wake up Alexa,” Brightfield says. “They do that using a microcontroller and a neural processing unit (NPU), and it still consumes hundreds of milliwatts of power.” If BrainChip’s solution indeed provides the claimed power savings for each device, the effect would be significant.
In a second demonstration, they used a similar machine learning model to demonstrate audio de-noising, for use in hearing aids or noise canceling headphones.
To date, neuromorphic computers have not found widespread commercial uses, and it remains to be seen if these miniature edge devices will take off, in part because of the diminished capabilities of such low-power AI applications. “If you’re at the very tiny neural network level, there’s just a limited amount of magic you can bring to a problem,” Intel’s Davis says.
BrainChip’s Brightfield, however, is hopeful that the application space is there. “It could be speech wake up. It could just be noise reduction in your earbuds or your AR glasses or your hearing aids. Those are all the kind of use cases that we think are targeted. We also think there’s use cases that we don’t know that somebody’s going to invent.”
Brain-like Computers Tackle the Extreme Edge
Start-up BrainChip announces a new chip design for a milliwatt-level AI inference
spectrum.ieee.org
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Hackster.io just revealed what the Akida Pico is all about:
View attachment 70198
BrainChip Shrinks the Akida, Targets Sub-Milliwatt Edge AI with the Neuromorphic Akida Pico
Second-generation Akida2 neuromorphic computing platform is now available in a battery-friendly form, targeting wearables and always-on AI.www.hackster.io
BrainChip Shrinks the Akida, Targets Sub-Milliwatt Edge AI with the Neuromorphic Akida Pico
Second-generation Akida2 neuromorphic computing platform is now available in a battery-friendly form, targeting wearables and always-on AI.
Gareth HalfacreeFollow
59 minutes ago • Machine Learning & AI / Wearables
https://events.hackster.io/impactspotlights
Edge artificial intelligence (edge AI) specialist BrainChip has announced a new entry in its Akida range of brain-inspired neuromorphic processors, the Akida Pico — claiming that it's the "lowest power acceleration coprocessor" yet developed, with eyes on the wearable and sensor-integrated markets.
"Like all of our Edge AI enablement platforms, Akida Pico was developed to further push the limits of AI on-chip compute with low latency and low power required of neural applications," claims BrainChip chief executive officer Sean Hehir of the company's latest unveiling. "Whether you have limited AI expertise or are an expert at developing AI models and applications, Akida Pico and the Akida Development Platform provides users with the ability to create, train and test the most power and memory efficient temporal-event based neural networks quicker and more reliably."
BrainChip has announced a new entry in its Akida family of neuromorphic processors, the tiny Akida Pico. (: BrainChip)
The Akida Pico is, as the name suggests, based on BrainChip's Akida platform — specifically, the second-generation Akida2. Like its predecessors, it uses neuromorphic processing technology inspired by the human brain to handle selected machine learning and artificial intelligence workloads with a high efficiency — but unlike its predecessors, the Akida Pico has been built to deliver the lowest possible power draw while still offering enough compute performance to be useful.
According to BrainChip, the Akida Pico draws under 1mW under load and uses power island design to offer a "minimal" standby power draw. Chips built around the core are also expected to be extremely small physically, ideal for wearables, thanks to a compact die area and customizable overall footprint through configurable data buffer and model parameter memory specifications. The part, its creators explain, is ideal for always-on AI in battery-powered or high-efficiency systems, where it can be used to wake a more powerful microcontroller or application processor when certain conditions are met.
The Akida Pico is based on the company's second-generation Akida2 platform, but tailored for sub-milliwatt power draw. (: BrainChip)
On the software side, the Akida Pico is supported by BrainChip's in-house MetaTF software flow — allowing the compilation and optimization of Temporal-Enabled Neural Networks (TENNs) for execution on the device. MetaTF also supports importation of existing models developed in TensorFlow, Keras, and PyTorch — meaning, BrainChip says, there's no need to learn a whole new framework to use the Akida Pico.
BrainChip has not yet announced plans to release Akida Pico in hardware, instead concentrating on making it available as Intellectual Property (IP) for others to integrate into their own chip designs; pricing had not been publicly disclosed at the time of writing.
More information is available on the BrainChip website.
energy efficiency
machine learning
artificial intelligence
wearables
gpio
Gareth HalfacreeFollow
Freelance journalist, technical author, hacker, tinkerer, erstwhile sysadmin. For hire: freelance@halfacree.co.uk.
No wonder there was a s/price rise over the last two days.
IMO, .. Surely this news is worthy of a ASX Co Price Sensitive Announcement or at the very least a ASX Co Update for the wider investor community and financial media outlets.
The way I see it is simple. A “price sensitive announcement” is one that the price of the shares would be sensitive too? Correct? So it’s a no brainer. We all know the price will be impacted if they announce this on the ASX so wouldn’t Brainchip have a legal obligation to announce as such??? I know history proves otherwise..
Euks
Regular
Mate!
We have belted the Old “price sensitive” horse into submission over the years and it’s now dead on the back straight of Flemington racecourse.
The only time we will ever get a price sensitive announcement is quarterly financials. Patents. And IP contracts with significant dollars attached, even the ESA/Airbus deal had money attached but wasn’t significant enough for its own stand alone announcement.
Finger crossed, Garmin, COROS or OURA sign a nice juicy contract for this new offering and our dead horse can get up off the ground. Lol
Euks
I'll give you a tip. It doesn't just sound stupid ... it is.
buena suerte :-)
BOB Bank of Brainchip
Keep going!! 
TheDrooben
Pretty Pretty Pretty Pretty Good
HarryCool1
Regular
Talk to me Esqy, how we lookin??
Oh and hey, how's things?
Oh and hey, how's things?
... but there is a slightly longer time to market ...
Good Afternoon HarryCool1 ,
Have not been at my computers , so lacking all the loose parameters / metrics which generally go into my price guidance......... predictions.
BUT.... the new PICO AKIDA should be an absolute ball tearer, absolute no brainer for any up and coming company to integrate on a new fangled product , if not already.
When the larger Company's will pull their head out and start implementing is anyone's guess.
Going for a STONKING GREEN FINNISH today.
* pressently doing gabian cage work , bit like Bravo , seems to be working.
Regards,
Esq.
buena suerte :-)
BOB Bank of Brainchip
Hi DB,Multiple websites are now promoting this news
What I want to know is...
View attachment 70211
How is AKIDA Pico, different from AKIDA-E ?
(both based on AKIDA 2.0 IP)
I'm guessing it's obviously smaller again?
But AKIDA-E is already from "1" node?..
Is this BrainChip going for the "low hanging fruit" ?..
View attachment 70212
(any similarity to an apple, is purely coincidental)
A node has 4 NPUs.
https://brainchip.com/wp-content/uploads/2023/03/BrainChip_second_generation_Platform_Brief.pdf
So that leaves open the possibility of, say, 1, 2 or 3 NPUs. Also the data can be recirculated through the nodes for additional processing if the first pass was insufficient to obtain classification/inference.
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OK,
So they have used a single NPE (nee NPU).
https://brainchip.com/wp-content/uploads/2024/10/BC_Akida_Pico_Brochure.pdf
buena suerte :-)
BOB Bank of Brainchip
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