BRN Discussion Ongoing

01. Okt. 2024 / 15:55

BrainChip’s Akida NPU: Redefining AI Processing with Event-Based Architecture​

Maurizio Di Paolo Emilio
6 min read
0

BrainChip has launched the Akida Pico, enabling the development of compact, ultra-low power, intelligent devices for applications in wearables, healthcare, IoT, defense, and wake-up systems, integrating AI into various sensor-based technologies. According to BrainChip, Akida Pico offers the lowest power standalone NPU core (less than 1mW), supports power islands for minimal standby power, and operates within an industry-standard development environment. It’s very small logic die area and configurable data buffer and model parameter memory help optimize the overall die size.

​

AI era​

In the sophisticated artificial intelligence (AI) era of today, including smart technology into consumer items is usually connected with cloud services, complicated infrastructure, and high expenses. Computational power and energy economy are occasionally in conflict in the realm of edge artificial intelligence. Designed for deep learning activities, traditional neural processing units (NPUs) require significant quantities of power, so they are less suited for always-on, ultra-low-power applications including sensor monitoring, keyword detection, and other extreme edge artificial intelligence uses. BrainChip is providing a fresh approach to this challenge.

BrainChip’s solution addresses one of the major challenges in edge AI: how to perform continuous AI processing without draining power. Traditional microcontroller-based AI solutions can manage low-power requirements but often lack the processing capability for complex AI tasks.



Steve Brightfield, CMO at BrainChip

2014 saw the launch of BrainChip, which took its inspiration from Peter Van Der Made’s work on neuromorphic computing concepts. Especially using spiking neural networks (SNNs), this technique replicates how the brain manages information, therefore transforming a fundamentally different method to traditional convolutional neural networks (CNNs). The SNN-based systems of BrainChip only compute when triggered by events rather than doing continuous calculations, hence optimizing power efficiency.

In an interview with Embedded, Steve Brightfield, CMO at BrainChip, talked about how this new method will change the game for ultra-low-power AI apps, showing big steps forward in the field. Brightfield said that this new technology makes it possible for common things like drills, hand tools, and other consumer products to have smart features without costing a lot more. “Today, a battery with a built-in tester can show how healthy it is with a simple color code: green means it’s good, red means it needs to be replaced. Providing a similar indicator, AI in these products can tell you when parts are wearing out before they break. BrainChip’s low-power, low-maintenance AI works in the background without being noticed, so advanced tests can be used by anyone without needing to know a lot about them,” Brightfield said.

Traditional NPUs vs. Event-Based Computing​

Brightfield claimed that ordinary NPUs—including those with multiplier-accumulator arrays—run on fixed pipelines, processing every input whether or not it is beneficial. Particularly in cases of sparse data, a typical occurrence in AI applications where most input values have little impact on the final outcome, this inefficacy often leads in wasted calculations. By use of an event-based computing architecture, BrainChip saves computational resources and electricity by activating calculations only when relevant data is present.
“Most NPUs keep calculating all data values, even for sparse data,” Brightfield remarked. “We schedule computations dynamically using our event-based architecture, so cutting out unnecessary processing.”

The Influence of Sparsity​

BrainChip’s main benefit comes from using data and neural weights’ sparsity. Traditional NPU architectures can take advantage of weight sparsity with pre-compilation, benefiting from model weight pruning, but cannot dynamically schedule for data sparsity, they must process all of the inputs.
By processing data just when needed, BrainChip’s SNN technology can drastically lower power usage based on the degree of sparsity in the data. BrainChip’s Akida NPU, for instance, could execute only when the sensor detects a significant signal in audio-based edge applications such as gunshot recognition or keyword detection, therefore conserving energy in the lack of acceptable data.

Akida Pico Block Diagram (Source: Brainchip)

​

Introducing the Akida Pico: Ultra-Low Power NPU for Extreme Edge AI​

Designed on a spiking neural network (SNN) architecture, BrainChip’s Akida Pico processor transforms event-based computing. Unlike conventional artificial intelligence models that demand constant processing capability, Akida runs just in response to particular circumstances. For always-on uses like anomaly detection or keyword identification, where power economy is vital, this makes it perfect. 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.

Wearables, IoT devices, and industrial sensors are among the jobs that call for continual awareness without draining the battery where the Akida Pico is well suited. Operating in the microwatt to milliwatt power range, this NPU is among the most efficient ones available; it surpasses even microcontrollers in several artificial intelligence applications.

For some always-on artificial intelligence uses, “the Akida Pico can be lower power than microcontrollers,” Brightfield said. “Every microamp counts in extreme battery-powered use cases, depending on how long it is intended to perform.”

The Akida Pico can stay always-on without significantly affecting battery life, whereas microcontroller-based AI systems often require duty cycling—turning the CPU off and on in bursts to save power. For edge AI devices that must run constantly while keeping a low power consumption, this benefit becomes very vital.

BrainChip’s MetaTF software flow allows developers to compile and optimize Temporal-Enabled Neural Networks (TENNs) on the Akida Pico. Supporting models created with TensorFlow/Keras and Pytorch, MetaTF eliminates the need to learn a new machine language framework, facilitating rapid AI application development for the Edge.





Akida Pico die area versus process (mm2) (Source: Brainchip)

​

Standalone Operation Without a Microcontroller​

Another remarkable feature of the Akida Pico is its ability to function alone, that is, without a host microcontroller to manage its tasks. Usually beginning, regulating, and halting operations using a microcontroller, the Akida Pico comprises an integrated micro-sequencer managing the full neural network execution on its own. This architecture reduces total system complexity, latency, and power consumption.

For applications needing a microcontroller, the Akida Pico is a rather useful co-processor for offloading AI tasks and lowering power requirements. From battery-powered wearables to industrial monitoring tools, this flexibility appeals to a wide range of edge artificial intelligence applications.

​

Targeting Key Edge AI Applications​

The ultra-low power characteristics of the Akida Pico help medical devices that need continuous monitoring—such as glucose sensors or wearable heart rate monitors—benefit.

Likewise, good candidates for this technology are speech recognition chores like voice-activated assistants or security systems listening for keywords. Edge artificial intelligence’s toughest obstacle is combining compute requirements with power consumption. In markets where battery life is crucial, the Akida Pico can scale performance while keeping inside limited power budgets.

One of the most notable uses of BrainChip’s artificial intelligence, according to Brightfield, is anomaly detection for motors or other mechanical systems Both costly and power-intensive, traditional methods monitor and diagnose equipment health using cloud-based infrastructure and edge servers. BrainChip embeds artificial intelligence straight within the motor or gadget, therefore flipping this concept on its head.

BrainChip’s ultra-efficient Akida Neural Processor Unit (NPU) for example, may continually examine vibration data from a motor. Should an abnormality, such as an odd vibration, be found, the system sets off a basic alert—akin to turning on an LED. Without internet access or a thorough examination, this “dumb and simple” option warns maintenance staff that the motor needs care instead of depending on distant servers or sophisticated diagnosis sites.

“In the field, a maintenance technician could only glance at the motor. Brightfield said, “they know it’s time to replace the motor before it fails if they spot a red light.” This method eliminates the need for costly software upgrades or cloud access, therefore benefiting equipment in distant areas where connectivity may be restricted.

Regarding keyword detection, BrainChip has included artificial intelligence right into the device. According to Brightfield, with 4-5% more accuracy than historical methods using raw audio data and modern algorithms, the Akida Pico uses just under 2 milliwatts of power to provide amazing results. Temporal Event-Based Neural Networks (TENNS), a novel architecture built from state space models that permits high-quality performance without the requirement for power-hungry microcontrollers, enable this achievement.

As demand for edge AI grows, BrainChip’s advancements in neuromorphic computing and event-based processing are poised to contribute significantly to the development of ultra-efficient, always-on AI systems, providing flexible solutions for various applications.

Tags:

• AI

Maurizio Di Paolo Emilio

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.

Frangipani said:

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.

Click to expand...

Frangipani said:

View attachment 70199

https://www.embedded.com/brainchips-akida-npu-redefining-ai-processing-with-event-based-architecture

01. Okt. 2024 / 15:55

BrainChip’s Akida NPU: Redefining AI Processing with Event-Based Architecture​

Maurizio Di Paolo Emilio
6 min read
0

BrainChip has launched the Akida Pico, enabling the development of compact, ultra-low power, intelligent devices for applications in wearables, healthcare, IoT, defense, and wake-up systems, integrating AI into various sensor-based technologies. According to BrainChip, Akida Pico offers the lowest power standalone NPU core (less than 1mW), supports power islands for minimal standby power, and operates within an industry-standard development environment. It’s very small logic die area and configurable data buffer and model parameter memory help optimize the overall die size.

​

AI era​

In the sophisticated artificial intelligence (AI) era of today, including smart technology into consumer items is usually connected with cloud services, complicated infrastructure, and high expenses. Computational power and energy economy are occasionally in conflict in the realm of edge artificial intelligence. Designed for deep learning activities, traditional neural processing units (NPUs) require significant quantities of power, so they are less suited for always-on, ultra-low-power applications including sensor monitoring, keyword detection, and other extreme edge artificial intelligence uses. BrainChip is providing a fresh approach to this challenge.

BrainChip’s solution addresses one of the major challenges in edge AI: how to perform continuous AI processing without draining power. Traditional microcontroller-based AI solutions can manage low-power requirements but often lack the processing capability for complex AI tasks.

Steve Brightfield, CMO at BrainChip

2014 saw the launch of BrainChip, which took its inspiration from Peter Van Der Made’s work on neuromorphic computing concepts. Especially using spiking neural networks (SNNs), this technique replicates how the brain manages information, therefore transforming a fundamentally different method to traditional convolutional neural networks (CNNs). The SNN-based systems of BrainChip only compute when triggered by events rather than doing continuous calculations, hence optimizing power efficiency.

In an interview with Embedded, Steve Brightfield, CMO at BrainChip, talked about how this new method will change the game for ultra-low-power AI apps, showing big steps forward in the field. Brightfield said that this new technology makes it possible for common things like drills, hand tools, and other consumer products to have smart features without costing a lot more. “Today, a battery with a built-in tester can show how healthy it is with a simple color code: green means it’s good, red means it needs to be replaced. Providing a similar indicator, AI in these products can tell you when parts are wearing out before they break. BrainChip’s low-power, low-maintenance AI works in the background without being noticed, so advanced tests can be used by anyone without needing to know a lot about them,” Brightfield said.

Traditional NPUs vs. Event-Based Computing​

Brightfield claimed that ordinary NPUs—including those with multiplier-accumulator arrays—run on fixed pipelines, processing every input whether or not it is beneficial. Particularly in cases of sparse data, a typical occurrence in AI applications where most input values have little impact on the final outcome, this inefficacy often leads in wasted calculations. By use of an event-based computing architecture, BrainChip saves computational resources and electricity by activating calculations only when relevant data is present.
“Most NPUs keep calculating all data values, even for sparse data,” Brightfield remarked. “We schedule computations dynamically using our event-based architecture, so cutting out unnecessary processing.”

The Influence of Sparsity​

BrainChip’s main benefit comes from using data and neural weights’ sparsity. Traditional NPU architectures can take advantage of weight sparsity with pre-compilation, benefiting from model weight pruning, but cannot dynamically schedule for data sparsity, they must process all of the inputs.
By processing data just when needed, BrainChip’s SNN technology can drastically lower power usage based on the degree of sparsity in the data. BrainChip’s Akida NPU, for instance, could execute only when the sensor detects a significant signal in audio-based edge applications such as gunshot recognition or keyword detection, therefore conserving energy in the lack of acceptable data.

Akida Pico Block Diagram (Source: Brainchip)

​

Introducing the Akida Pico: Ultra-Low Power NPU for Extreme Edge AI​

Designed on a spiking neural network (SNN) architecture, BrainChip’s Akida Pico processor transforms event-based computing. Unlike conventional artificial intelligence models that demand constant processing capability, Akida runs just in response to particular circumstances. For always-on uses like anomaly detection or keyword identification, where power economy is vital, this makes it perfect. 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.

Wearables, IoT devices, and industrial sensors are among the jobs that call for continual awareness without draining the battery where the Akida Pico is well suited. Operating in the microwatt to milliwatt power range, this NPU is among the most efficient ones available; it surpasses even microcontrollers in several artificial intelligence applications.

For some always-on artificial intelligence uses, “the Akida Pico can be lower power than microcontrollers,” Brightfield said. “Every microamp counts in extreme battery-powered use cases, depending on how long it is intended to perform.”

The Akida Pico can stay always-on without significantly affecting battery life, whereas microcontroller-based AI systems often require duty cycling—turning the CPU off and on in bursts to save power. For edge AI devices that must run constantly while keeping a low power consumption, this benefit becomes very vital.

BrainChip’s MetaTF software flow allows developers to compile and optimize Temporal-Enabled Neural Networks (TENNs) on the Akida Pico. Supporting models created with TensorFlow/Keras and Pytorch, MetaTF eliminates the need to learn a new machine language framework, facilitating rapid AI application development for the Edge.
Akida Pico die area versus process (mm2) (Source: Brainchip)

​

Standalone Operation Without a Microcontroller​

Another remarkable feature of the Akida Pico is its ability to function alone, that is, without a host microcontroller to manage its tasks. Usually beginning, regulating, and halting operations using a microcontroller, the Akida Pico comprises an integrated micro-sequencer managing the full neural network execution on its own. This architecture reduces total system complexity, latency, and power consumption.

For applications needing a microcontroller, the Akida Pico is a rather useful co-processor for offloading AI tasks and lowering power requirements. From battery-powered wearables to industrial monitoring tools, this flexibility appeals to a wide range of edge artificial intelligence applications.

​

Targeting Key Edge AI Applications​

The ultra-low power characteristics of the Akida Pico help medical devices that need continuous monitoring—such as glucose sensors or wearable heart rate monitors—benefit.

Likewise, good candidates for this technology are speech recognition chores like voice-activated assistants or security systems listening for keywords. Edge artificial intelligence’s toughest obstacle is combining compute requirements with power consumption. In markets where battery life is crucial, the Akida Pico can scale performance while keeping inside limited power budgets.

One of the most notable uses of BrainChip’s artificial intelligence, according to Brightfield, is anomaly detection for motors or other mechanical systems Both costly and power-intensive, traditional methods monitor and diagnose equipment health using cloud-based infrastructure and edge servers. BrainChip embeds artificial intelligence straight within the motor or gadget, therefore flipping this concept on its head.

BrainChip’s ultra-efficient Akida Neural Processor Unit (NPU) for example, may continually examine vibration data from a motor. Should an abnormality, such as an odd vibration, be found, the system sets off a basic alert—akin to turning on an LED. Without internet access or a thorough examination, this “dumb and simple” option warns maintenance staff that the motor needs care instead of depending on distant servers or sophisticated diagnosis sites.

“In the field, a maintenance technician could only glance at the motor. Brightfield said, “they know it’s time to replace the motor before it fails if they spot a red light.” This method eliminates the need for costly software upgrades or cloud access, therefore benefiting equipment in distant areas where connectivity may be restricted.

Regarding keyword detection, BrainChip has included artificial intelligence right into the device. According to Brightfield, with 4-5% more accuracy than historical methods using raw audio data and modern algorithms, the Akida Pico uses just under 2 milliwatts of power to provide amazing results. Temporal Event-Based Neural Networks (TENNS), a novel architecture built from state space models that permits high-quality performance without the requirement for power-hungry microcontrollers, enable this achievement.

As demand for edge AI grows, BrainChip’s advancements in neuromorphic computing and event-based processing are poised to contribute significantly to the development of ultra-efficient, always-on AI systems, providing flexible solutions for various applications.

Tags:

• AI

Maurizio Di Paolo Emilio

Click to expand...

Well, I suppose the tiny Akida Pico (respectively pre-announcement leakage relating to it) may have been the real reason for yesterday’s massive spike in share price….

​

Antonio J. Viana, the Chairman of our Board of Directors, has joined Parsley’s Board of Directors:

“Parsley360 (Parsley), is a human centered, AI-enabled performance optimization system with universal access to an emotionally intelligent pocket coach for all employees.”

Frangipani said:

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.

Click to expand...

Multiple websites are now promoting this news

What I want to know is...



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" ?..



(any similarity to an apple, is purely coincidental)

Tothemoon24 said:

View attachment 70174

Click to expand...

What happened with the last reveal they had or was I dreaming that?

Okay so now I guess we’re supposed to wait Another few years for customers to trial akida pico are we?

Tothemoon24 said:

https://brainchip.com/wp-content/uploads/2024/10/BC_Akida_Pico_Brochure.pdf?utm_source=website&utm_medium=landing+page&utm_campaign=Akida+Pico+Announcement

View attachment 70221 View attachment 70222

Click to expand...

Your don't have to post this. People already did in here

genyl said:

Your don't have to post this. People already did in here

Click to expand...

Cheers skipper

I wonder if this is considered a 'price sensitive announcement' - given their track record, I won't hold my breath..

NickBRN said:

I wonder if this is considered a 'price sensitive announcement' - given their track record, I won't hold my breath..

Click to expand...

Given none of our other product announcements have resulted in any form of income I doubt this one would tick the box for price sensitive given the companies stance on what they need to announce in that manner.

I dont post often, but watch alot (creepy sorry) Well, this i am excited about. I can see this iteration selling in volume. This is where BRN will see revenue to sustain their higher goals of umbiqutous AI (in my opinion).

How long it takes to sell? 1 day, 10 years?

But hoping, this was requested by an interested party that make headphones, hearing aids or cameras etc and discussions are well advanced (in my opinion).

Event based (high security, low power), wake up device, noise reduction, biomed. It has so many applications. I wouldn't be surprised if this is just the release date and will be followed by some quick fire contract signings (in my opinion).

I always use a good friend's business analogy. Back in the 90s he had a juice kiosk stand at a university. Great business plan and well researched. He was going bankrupt though and not selling juices (much like you would see at a boost juice). Anyway they decided to sell coffee as well. They became successful overnight with long lines for their coffee (the coffee dependancy in australia had just started to kick in). Many new kiosks openned since, but now, they have gone back to selling juices because that is more popular. Its all about the right product at the right time, in the right place at the right price (basics of marketing).

I believe we are ahead of the market with our akida 2.0. It's a game changing product, but the market is not ready for such a monumental shift (in my opinion). The pico will give BRN a chance to get their name out there and prove themselves in lower risk settings. Aiming for self driving cars and data centres should always be the aim. But this is a smart move.

DYOR.

DingoBorat said:

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)

Click to expand...

Hi Dingo,

your post reminded me of something Sean had said during the February Virtual Investor Roadshow and then repeated in his AGM address in May - words that left me somewhat puzzled, as I would have assumed 1 node to be the bare minimum… Apparently not so.

“All of that comes down with a very flexible IP model that can address the most small [sic], the smallest use cases in a single node, or quite frankly even smaller, up to 256 nodes, allow you to address the large broadness of the edge market outside the data center.” Virtual Investor Roadshow (from 15:37 min)

(That first time I was actually wondering whether he had simply misspoken.)

“Finally, key vertical markets including industrial - for surveillance, vision, Gen AI, predictive maintenance, automotive - for in cabin, ADAS, connected sensors, and general medical - for predictive diagnostics, continue to push the limits of edge-first computing. In these use cases the ability to scale up and down to exceptionally small configurations is a key requirement. Akida has the unique capability to scale down to less than single node configurations making it the ideal choice for this market.” CEO address at the 2024 AGM (from 12:44 min)

So yes, I am pretty sure that means Akida Pico will be even smaller than Akida-E.
It is a bit unfortunate, though, that we already have Akida-P (as in maximum performance) towards the network edge, as we now can’t just shorten Akida Pico to Akida-P (although personally I much prefer the catchy name Akida Pico anyway). Possibly Akida-Max would have been a better choice than Akida-P?
Or alternatively to have labelled the Akida 2.0 offerings S - M - L, similar to clothes sizes, which would still have left options such as XS or XL to the far left and right of that scale? Just my opinion, plus it’s too late now anyway…

By the way: If you go back to Episode 6 of the Quarterly Investor Podcast and listen to what Sean says from 17:27 min, he was alluding to Akida Pico as well:

“It also allows us to actually package up keyword spotting, which is usually in very small models, with a very unique configuration we have on our Akida 2.0 (…) So, keyword spotting is a very good use case for TENNs and Akida together.”

Picoing lots of low-hanging fruit alongside the likes of Innatera’s T1 and SynSense’s Xylo would be my guess… Possibly even picoing some of that fruit somewhat unexpectedly from under their noses now? But given the size of the extreme Edge AI market, there should be a bountiful harvest for all those diligent fruit pickers.

Frangipani said:

Hi Dingo,

your post reminded me of something Sean had said during the February Virtual Investor Roadshow and then repeated in his AGM address in May - words that left me somewhat puzzled, as I would have assumed 1 node to be the bare minimum… Apparently not so.

“All of that comes down with a very flexible IP model that can address the most small [sic], the smallest use cases in a single node, or quite frankly even smaller, up to 256 nodes, allow you to address the large broadness of the edge market outside the data center.” Virtual Investor Roadshow (from 15:37 min)

(That first time I was actually wondering whether he had simply misspoken.)

“Finally, key vertical markets including industrial - for surveillance, vision, Gen AI, predictive maintenance, automotive - for in cabin, ADAS, connected sensors, and general medical - for predictive diagnostics, continue to push the limits of edge-first computing. In these use cases the ability to scale up and down to exceptionally small configurations is a key requirement. Akida has the unique capability to scale down to less than single node configurations making it the ideal choice for this market.” CEO address at the 2024 AGM (from 12:44 min)

So yes, I am pretty sure that means Akida Pico will be even smaller than Akida-E.
It is a bit unfortunate, though, that we already have Akida-P (as in maximum performance) towards the network edge, as we now can’t just shorten Akida Pico to Akida-P (although personally I much prefer the catchy name Akida Pico anyway). Possibly Akida-Max would have been a better choice than Akida-P?
Or alternatively to have labelled the Akida 2.0 offerings S - M - L, similar to clothes sizes, which would still have left options such as XS or XL to the far left and right of that scale? Just my opinion, plus it’s too late now anyway…

By the way: If you go back to Episode 6 of the Quarterly Investor Podcast and listen to what Sean says from 17:27 min, he was alluding to Akida Pico as well:

“It also allows us to actually package up keyword spotting, which is usually in very small models, with a very unique configuration we have on our Akida 2.0 (…) So, keyword spotting is a very good use case for TENNs and Akida together.”

Picoing lots of low-hanging fruit alongside the likes of Innatera’s T1 and SynSense’s Xylo would be my guess… Possibly even picoing some of that fruit somewhat unexpectedly from under their noses now? But given the size of the extreme Edge AI market, there should be a bountiful harvest for all those diligent fruit pickers.

Click to expand...

Some prodigious research there Frangipani, as always

What I like about what you brought to the surface, is the fact that like TENNs, AKIDA Pico has "already" been known to the "customers" we are dealing with, for some Time, which shortens the "lead time" for product developments.

Some of which, will hopefully break the surface soon.

Could someone else please post another link about pico, tyia