BRN Discussion Ongoing

[Stable Genius]

I think it also came with a free bottle of DDT to wash one's hair with also.
View attachment 22496

@Rise from the ashes

Not sure about washing your hair with DDT as it didn’t help the Bald Eagles.

 

[Fact Finder]

The radiation levels apparently only equated to a days worth of sunshine.

One day of direct sun exposure can only cause skin cancer so that is a great comfort.

 

[Learning]

From this post yesterday, I don't think the partnership with Mercedes and NVIDIA is ending anytime soon.

Magnus Östberg on LinkedIn: #mercedesbenz #automotivesoftware

With partners like NVIDIA, we’re able to unleash the full potential of #MercedesBenz. Without cooperation and collaboration at eye level, #AutomotiveSoftware…

www.linkedin.com

View attachment 22489

Learning
PS, sorry to hear about your car problem.

Didn't see this yesterday, but our resident Verification Engineer is supporting the cause @chapman89

Learning.

 

[The Pope]

Hi @Proga

This is the recently read article link:

Qualcomm partners with Mercedes-Benz and Red Hat to accelerate its automotive chip ambitions - SiliconANGLE

Qualcomm partners with Mercedes-Benz and Red Hat to accelerate its automotive chip ambitions - SiliconANGLE

siliconangle.com

I have reproduced the following paragraph because it references the effect and invite others to recall the statements of gratitude by Brainchip to Mercedes Benz for mentioning AKIDA technology:


“Constellation Research Inc. analyst Holger Mueller told SiliconANGLE that Qualcomm’s push shows us that chipmakers are no longer focused solely on servers, computers and smartphones, but also cars, which are evolving to become powerful compute platforms too. “The battle goes beyond the hardware, as car manufacturers want to see who can provide a complete platform for car operations, infotainment, self-driving, maintenance, connectivity and more,” Mueller said. “Today it’s Qualcomm’s turn to show what it can do, announcing a partnership with Red Hat and customer win with Mercedes-Benz.

When companies land a big win with a premium car manufacturer that often has a halo effect within the rest of the industry, so it’s something that bodes well for the prospects Qualcomm’s automotive platforms.”

My opinion only DYOR
FF

AKIDA BALLISTA

Hi FF and others

How much of the following may result in big tech companies (not limiting to) strong arming others for the near future in rolling out say BRN revolutionary neuromorphic technology.

The way I look at it is some very big tech companies have invested heavily with cloud data transfer / storage over the past number of years as it was considered the way of the future? Would it be expected in some way that atleast some big tech companies are potentially coming to back room agreements with others to not lose out big time so delay delay delay the transition to like BRN neuromorphic (not requiring cloud) and when ready in a number of years from now we can phase out a reasonable % of cloud storage. We have to remember that the cloud storage business is big revenue maker as I think most corporations and general businesses etc use or rely on the cloud for nearly all general business.
If a tech company spends 100s millions. You don’t want to go to your share holders and say it looks like we blew 100s millions on nothing.
Just my thoughts and I’m sure this may start a nice debate with some.

Has this ever been discussed in this forum before?

Edit - The global cloud computing market size was valued at USD 405.65 billion in 2021. The market is projected to grow from USD 480.04 billion in 2022 to USD 1,712.44 billion by 2029, exhibiting a CAGR of 19.9% during the forecast period.

 

[Quercuskid]

Were you glowing after using them?

Still am

 

[wilzy123]

This is all fantastic progress. The Valeo partnership is one that I have been expecting to hear more detail about. What I wasn't expecting was the detail highlighted below. So as good as these partnerships are, until Brainchip's name is inked in more contracts... people are entitled to varying opinions. It's not gaslighting this community, not fear mongering, but simply highlighting some very recent factual announcements. I'm sure with partners such as ARM, that these contracts will come in time. But it's the surprise attacks, such as the highlighted text below, from potential competitors... that has me a little concerned as to how much time Brainchip will take? 

 

[Stable Genius]

Hi FF and others

How much of the following may result in big tech companies (not limiting to) strong arming others for the near future in rolling out say BRN revolutionary neuromorphic technology.

The way I look at it is some very big tech companies have invested heavily with cloud data transfer / storage over the past number of years as it was considered the way of the future? Would it be expected in some way that atleast some big tech companies are potentially coming to back room agreements with others to not lose out big time so delay delay delay the transition to like BRN neuromorphic (not requiring cloud) and when ready in a number of years from now we can phase out a reasonable % of cloud storage. We have to remember that the cloud storage business is big revenue maker as I think most corporations and general businesses etc use or rely on the cloud for nearly all general business.
If a tech company spends 100s millions. You don’t want to go to your share holders and say it looks like we blew 100s millions on nothing.
Just my thoughts and I’m sure this may start a nice debate with some.

Has this ever been discussed in this forum before?

Edit - The global cloud computing market size was valued at USD 405.65 billion in 2021. The market is projected to grow from USD 480.04 billion in 2022 to USD 1,712.44 billion by 2029, exhibiting a CAGR of 19.9% during the forecast period.

@prnewy74 There are always going to be some companies heavily invested that are going to hold out with what they have to reap the profits from their investment. That said; the change to edge inferencing isn’t going to happen overnight. I would think it would still take several e.g 5 plus years to really make a large percent difference.

I am hopeful the market share will be enough to build Brainships SP sufficiently along the journey.

However companies like Dell are well aware of the advantages of Akida in data centres (as per the Dell podcast) and I would be very surprised if they aren’t an early mover in this area. Fingers crossed!

There will also be companies that aren’t as heavily invested in the old system and will be chaffing at the bit to create/develop this new technology and get ahead of their competition. A bit like Kodak versus digital cameras really.

I see Brainchip in the box seat because once some companies incorporate us and provide what others can’t then the heat will be on the rest to catch up; and Brainchip will be waiting, via ARM, MEGACHIPS, SIFIVE etc with waiting arms.

 

[HopalongPetrovski]

We must trust the science at all times no matter the cost.

 

[Rise from the ashes]

Still am

Wonder if they still had charlie in the recipe then.

 

[Diogenese]

This is all fantastic progress. The Valeo partnership is one that I have been expecting to hear more detail about. What I wasn't expecting was the detail highlighted below. So as good as these partnerships are, until Brainchip's name is inked in more contracts... people are entitled to varying opinions. It's not gaslighting this community, not fear mongering, but simply highlighting some very recent factual announcements. I'm sure with partners such as ARM, that these contracts will come in time. But it's the surprise attacks, such as the highlighted text below, from potential competitors... that has me a little concerned as to how much time Brainchip will take? View attachment 22485

I don't see the Snapdragon deal as excluding Akida if the Snapdrgon is used for the central management system. Valeo foreshadows a hierarchy of processors including domain and zone processors and smart sensors as well as the central processor.

https://www.valeo.com/en/domain-controller/
A move towards centralized electrical/electronic architecture is on the rise in the automotive industry with domain and zonal controller-based architecture acting as precursors. Centralization through a single vehicle domain controller aims to:

• reduce complexity & cabling requirements
• decrease costs & power consumption
• enable scalability & software updates

This trend of automotive domain controllers is especially driven by automation, which requires the synthesis of data from multiple sensors, high-speed processing, and the need to respect strict functional safety requirements. It also has to be able to offer continuous development of new features and functionality over the vehicle’s lifetime through OTA (Over The Air) updates.
...
Valeo, a world market leader in driving assistance systems with over 30 years of expertise in this sector, is leading the way and supporting our clients by offering a broad range of domain and zone controller solutions for the automotive market.
Valeo’s ADAS domain controller collects data from the various interior and exterior smart sensors equipped on the vehicle (automated parking system, LiDAR sensors…) to create a highly accurate 360° model of the vehicle’s environment. Using sophisticated processing algorithms, the ADAS system directs safe vehicle behavior and offers high performance on the road. It is based on a fully open architecture, allowing functionality to be supplied by Valeo or third parties.
Key characteristics include:

• Cost-efficient architecture
• Scalable solution allowing for software upgrades
• Flexibility of the system-on-chip
• Passive, Active and Liquid cooled Thermal Management [!?]

 

[Diogenese]

I think it also came with a free bottle of DDT to wash one's hair with also.
View attachment 22496

Gentlemen: I need replacements for the following radioactive sources:
U235
Please notify me of the cost and date of mailing.

Name: ... Master V. Putin (aged 8)

Street: Igor Vasil'evich Kurchatov Prospect
City: Semipalatinsk
Zone: Secret
State: Security

 

[manny100]

Hi FF and others

How much of the following may result in big tech companies (not limiting to) strong arming others for the near future in rolling out say BRN revolutionary neuromorphic technology.

The way I look at it is some very big tech companies have invested heavily with cloud data transfer / storage over the past number of years as it was considered the way of the future? Would it be expected in some way that atleast some big tech companies are potentially coming to back room agreements with others to not lose out big time so delay delay delay the transition to like BRN neuromorphic (not requiring cloud) and when ready in a number of years from now we can phase out a reasonable % of cloud storage. We have to remember that the cloud storage business is big revenue maker as I think most corporations and general businesses etc use or rely on the cloud for nearly all general business.
If a tech company spends 100s millions. You don’t want to go to your share holders and say it looks like we blew 100s millions on nothing.
Just my thoughts and I’m sure this may start a nice debate with some.

Has this ever been discussed in this forum before?

Edit - The global cloud computing market size was valued at USD 405.65 billion in 2021. The market is projected to grow from USD 480.04 billion in 2022 to USD 1,712.44 billion by 2029, exhibiting a CAGR of 19.9% during the forecast period.

Not sure whether this is the case here in the shorter term but in general plenty of superior products have fallen by the wayside simply because the cost of utilisation did not match the gains of using them.
With AI at the Edge once it starts moving it should gain speed very quickly. Only need one large adoptedrand the others are forced to follow suit.

 

[Rise from the ashes]

Gentlemen: I need replacements for the following radioactive sources:
U235
Please notify me of the cost and date of mailing.

Name: ... Master V. Putin (aged 8)

Street: Igor Vasil'evich Kurchatov Prospect
City: Semipalatinsk
Zone: Secret
State: Security

That was brilliant.
Following on with the comedy theme.


Enjoy. His account has now been reinstated

Check out Donald J. Trump on Twitter.
45th President of the United States of America https://twitter.com/realDonaldTrump?s=20&t=D7Fn6vVd-kY4EuQ0VkfbSw

 

[The Pope]

@prnewy74 There are always going to be some companies heavily invested that are going to hold out with what they have to reap the profits from their investment. That said; the change to edge inferencing isn’t going to happen overnight. I would think it would still take several e.g 5 plus years to really make a large percent difference.

I am hopeful the market share will be enough to build Brainships SP sufficiently along the journey.

However companies like Dell are well aware of the advantages of Akida in data centres (as per the Dell podcast) and I would be very surprised if they aren’t an early mover in this area. Fingers crossed!

There will also be companies that aren’t as heavily invested in the old system and will be chaffing at the bit to create/develop this new technology and get ahead of their competition. A bit like Kodak versus digital cameras really.

I see Brainchip in the box seat because once some companies incorporate us and provide what others can’t then the heat will be on the rest to catch up; and Brainchip will be waiting, via ARM, MEGACHIPS, SIFIVE etc with waiting arms.

Thanks SG. I understand and agree with your thoughts and also have my fingers crossed with Dell etc
Thanks for responding to my post quickly. Much appreciated.

 

[Rise from the ashes]

CSDL | IEEE Computer Society

www.computer.org

 

[Fact Finder]

Thanks SG. I understand and agree with your thoughts and also have my fingers crossed with Dell etc
Thanks for responding to my post quickly. Much appreciated.

Hi @prnewy74
The issue has been discussed from time to time but I personally think it is a non issue for the Edge and hence AKIDA:

1. The Cloud is as you point out about storage of data;

2. The value of relevant data cannot be overstated;

3. The present acquisition of data however accumulates relevant data but hides it under tonnes and tonnes of irrelevant data;

4. Michael Dell and others have predicted that 75% of data is going to be collected at the Edge by 2025;

5. AKIDA technology allows for the collection of relevant data and relevant data has commercial value stored intelligently in the cloud.

So AKIDA offers these tech giants the ability to fully monetise cloud stored data.

My opinion only DYOR
FF

AKIDA BALLISTA

 

[Learning]

Has we discusse about 'innatera'

From the untrained eye, they are trying to do what Brainchip is doing. Although NONE of their patients is has been grant. Our more knowledgeable shareholders can investigate.

Innatera | Ultra low power intelligence for the sensor edge.

www.innatera.com

Google Patents

Search and read the full text of patents from around the world with Google Patents, and find prior art in our index of non-patent literature.

patents.google.com

Edit: Further looking in this, I think they are using 'Analog'.

Learning

 

[Fullmoonfever]

Nice little article on Edge Impulse / BRN in IOT World espousing the merits of both...most of which we know already.

Maybe posted previously?

Edge Impulse – Making Machine Learning Available for Embedded Devices

Edge Impulse is a software as a service (SaaS) platform that uses a compiler to turn TensorFlow Lite models into C++ programs. The platform works with existing tools and is designed to compete with startups. This article explores Edge Impulse's unique capabilities and competitive positioning...

www.iotworlds.com

​

Artificial IntelligenceMachine Learning

Edge Impulse – Making Machine Learning Available for Embedded Devices​

Edge Impulse is a software as a service (SaaS) platform that uses a compiler to turn TensorFlow Lite models into C++ programs. The platform works with existing tools and is designed to compete with startups. This article explores Edge Impulse’s unique capabilities and competitive positioning.

Edge Impulse is a software as a service platform​

Edge Impulse is a software as ta service platform that makes machine learning available for embedded devices. Launched in mid-2019, the platform already boasts a growing list of enterprise customers including Oura, Polycom, and NASA. Its goal is to help customers deploy machine learning on their embedded devices and achieve high-impact results.

Edge Impulse also has a relationship with Arm. Shelby’s previous startup, Sensinode, was acquired by Arm in 2013. Sensinode provided low-power mesh networking and Internet gateway systems. The two companies were able to work together on end-to-end solutions that covered tel infrastructure and embedded devices. The acquisition gave Arm access to a range of compute power.

Edge Impulse is available as a free SaaS platform for developers. It includes all of the steps needed to build a machine-learning model, from data collection to signal processing and deployment to the sensor. It is free to use for individual developers, but there is a paid version for enterprise customers. The SaaS platform is a powerful tool for embedded engineers looking to make machine-learning solutions for their applications.

Edge Impulse is the leading development platform for machine learning on edge devices. It simplifies the process of developing and testing ML models on edge devices by streamlining data collection and integration. It then validates the models against real-world data. And finally, it deploys optimized models to edge targets, unlocking massive value across every industry. With the platform, millions of developers and businesses can now build and deploy machine-learning applications on billions of devices.

Edge Impulse has received several awards for its EON Tuner, an algorithm that automatically selects the most suitable machine learning model for the edge. It also supports the BrainChip MetaTF platform, which helps developers quickly develop enterprise-grade ML algorithms. To learn more about Edge Impulse, check out the free hour-long webinar.

It uses a compiler that converts TensorFlow Lite models into human readable C++ programs​

Edge Impulse is a platform that uses a Tensorflow Lite compiler to build deep learning models on embedded devices. The resulting model can be deployed to any device, whether it be a smartphone, tablet, or PC. It is a cross-platform and open-source platform that makes it easy to train models and deploy them at the Edge. It works on Linux-based embedded devices and mobile devices.

Edge Impulse works with TensorFlow Lite, an open-source deep learning framework. It is designed for on-device machine learning inference, and it is lightweight and low-latency. Its architecture allows for efficient model conversion, and it uses a compiler that translates TensorFlow Lite models into human-readable C++ programs. This allows it to run on a wide range of hardware, including devices with low-power MCUs.

The TinyML algorithm is designed to detect three different types of geometry. Edge Impulse implements it with its C++ SDK and TensorFlow support. It can also be deployed using a custom PCB. It can also run in standby mode. In addition, TinyML models can be used to filter sensor data.

The Edge Impulse SDK provides a number of useful examples. For instance, the vacuum-recognition demo contains examples and data. This data can be downloaded separately from the GitHub repository. The data used for this demonstration is the COCO dataset.

The model is optimized for low latency, which is important when it is deployed at the edge. By reducing the computational costs, it is possible to produce a model that uses less memory. Optimizing the model reduces its size while preserving its accuracy. Moreover, it allows for a model to store its data as graphs or 32-bit floating-point values.

Edge Impulse also provides support for data forwarding. By leveraging UART connectivity, users can use the CLI to classify sensor data. The Edge Impulse studio also enables customization of data processing, learning, and optimization.

Edge Impulse can also be used to build ML models. This platform has a range of built-in tools and libraries that will make it easy to train ML models. Its CLI supports capturing data from serial ports, CSV files, and JSON files.

It integrates seamlessly with existing tools​

With Edge Impulse, you can build AI applications using familiar and well documented methods. The tools in this software suite can be combined to achieve a variety of goals, from detecting anomalies to analyzing signal patterns. They provide several different analysis methods, including signal flattening and analysis of repetitive motion.

The software also allows you to build custom models without coding. There are 3 basic building blocks you must use to build a model. The first one, input block, is used to specify the type of data you want to input to the model. This can be images or time series.

Edge Impulse’s AI platform is available as a free and enterprise version. The free version has some limitations, such as a single developer’s sweat and a cloud storage limit of four GB. The enterprise version, which costs $149 per project, removes these restrictions and allows for up to five users per project.

Edge Impulse enables the development of enterprise-grade ML algorithms that train on real sensor data. These models can be quantised and optimised. Then, they can be deployed on BrainChip Akida devices. Enterprise developers can also leverage the BrainChip MetaTF model deployment block to deploy neuromorphic models.

Edge Impulse is free and easy to use. It helps speed up data pre-processing and model building. It features a user-friendly UI that guides you through the process and allows you to customize your model. It also provides a TensorFlow-lite model library that supports all popular formats.

Edge Impulse’s AI technology is based on the BrainChip Akida processor, a breakthrough neural networking processor architecture that delivers high performance and ultra-low power, while still allowing for on-chip learning. It also enables you to visualize the results of your inference using any web browser.

It competes with startups​

Edge Impulse is a startup that uses machine learning to build smarter embedded devices. The company launched in mid-2019, and has almost 30,000 developers using its platform. Its customers include NASA, Polycom, and Advantech. In a recent funding round, Edge Impulse raised $34 million from investors including Coatue, Momenta Ventures, and Acrew Capital.

The startup uses off-the-shelf machine learning frameworks such as TensorFlow to make its models as easy to use as possible. It also provides tools for domain experts to collect data, classify it, and predict the future. Those features are also available in the free tier of Edge Impulse. The company also offers a subscription option that allows customers to gain access to features like collaboration between multiple engineers, larger datasets, and model versioning.

Edge Impulse’s platform makes it easier to build smarter IoT applications. It supports sensor, audio, and computer vision applications. It can also help with asset tracking and health applications. In addition, it ingests 99 percent of critical sensor data, which improves the performance of its algorithms. This technology also enables developers to quickly and easily create new applications.

Edge Impulse has recently raised $34 million in Series B funding. This investment will allow the startup to expand its operations, marketing, and staff. The company also plans to double its annual recurring revenue and triple its market valuation by 2022. Its current investors include Coatue, Sequoia Capital, and Accel.

As a SaaS platform, the company offers developers a solution to implement TinyML in their enterprise environments. Its SaaS platform includes the entire set of steps that is necessary to build models: data collection, signal processing, and deployment to a sensor. It’s available for free to individual developers, as well as a paid service for enterprise customers.

 

[Izzzzzzzzzzy]

Nice little article on Edge Impulse / BRN in IOT World espousing the merits of both...most of which we know already.

Maybe posted previously?

Edge Impulse – Making Machine Learning Available for Embedded Devices

Edge Impulse is a software as a service (SaaS) platform that uses a compiler to turn TensorFlow Lite models into C++ programs. The platform works with existing tools and is designed to compete with startups. This article explores Edge Impulse's unique capabilities and competitive positioning...

www.iotworlds.com

View attachment 22506

​

Artificial IntelligenceMachine Learning

Edge Impulse – Making Machine Learning Available for Embedded Devices​

Edge Impulse is a software as a service (SaaS) platform that uses a compiler to turn TensorFlow Lite models into C++ programs. The platform works with existing tools and is designed to compete with startups. This article explores Edge Impulse’s unique capabilities and competitive positioning.

Edge Impulse is a software as a service platform​

Edge Impulse is a software as ta service platform that makes machine learning available for embedded devices. Launched in mid-2019, the platform already boasts a growing list of enterprise customers including Oura, Polycom, and NASA. Its goal is to help customers deploy machine learning on their embedded devices and achieve high-impact results.

Edge Impulse also has a relationship with Arm. Shelby’s previous startup, Sensinode, was acquired by Arm in 2013. Sensinode provided low-power mesh networking and Internet gateway systems. The two companies were able to work together on end-to-end solutions that covered tel infrastructure and embedded devices. The acquisition gave Arm access to a range of compute power.

Edge Impulse is available as a free SaaS platform for developers. It includes all of the steps needed to build a machine-learning model, from data collection to signal processing and deployment to the sensor. It is free to use for individual developers, but there is a paid version for enterprise customers. The SaaS platform is a powerful tool for embedded engineers looking to make machine-learning solutions for their applications.

Edge Impulse is the leading development platform for machine learning on edge devices. It simplifies the process of developing and testing ML models on edge devices by streamlining data collection and integration. It then validates the models against real-world data. And finally, it deploys optimized models to edge targets, unlocking massive value across every industry. With the platform, millions of developers and businesses can now build and deploy machine-learning applications on billions of devices.

Edge Impulse has received several awards for its EON Tuner, an algorithm that automatically selects the most suitable machine learning model for the edge. It also supports the BrainChip MetaTF platform, which helps developers quickly develop enterprise-grade ML algorithms. To learn more about Edge Impulse, check out the free hour-long webinar.

It uses a compiler that converts TensorFlow Lite models into human readable C++ programs​

Edge Impulse is a platform that uses a Tensorflow Lite compiler to build deep learning models on embedded devices. The resulting model can be deployed to any device, whether it be a smartphone, tablet, or PC. It is a cross-platform and open-source platform that makes it easy to train models and deploy them at the Edge. It works on Linux-based embedded devices and mobile devices.

Edge Impulse works with TensorFlow Lite, an open-source deep learning framework. It is designed for on-device machine learning inference, and it is lightweight and low-latency. Its architecture allows for efficient model conversion, and it uses a compiler that translates TensorFlow Lite models into human-readable C++ programs. This allows it to run on a wide range of hardware, including devices with low-power MCUs.

The TinyML algorithm is designed to detect three different types of geometry. Edge Impulse implements it with its C++ SDK and TensorFlow support. It can also be deployed using a custom PCB. It can also run in standby mode. In addition, TinyML models can be used to filter sensor data.

The Edge Impulse SDK provides a number of useful examples. For instance, the vacuum-recognition demo contains examples and data. This data can be downloaded separately from the GitHub repository. The data used for this demonstration is the COCO dataset.

The model is optimized for low latency, which is important when it is deployed at the edge. By reducing the computational costs, it is possible to produce a model that uses less memory. Optimizing the model reduces its size while preserving its accuracy. Moreover, it allows for a model to store its data as graphs or 32-bit floating-point values.

Edge Impulse also provides support for data forwarding. By leveraging UART connectivity, users can use the CLI to classify sensor data. The Edge Impulse studio also enables customization of data processing, learning, and optimization.

Edge Impulse can also be used to build ML models. This platform has a range of built-in tools and libraries that will make it easy to train ML models. Its CLI supports capturing data from serial ports, CSV files, and JSON files.

It integrates seamlessly with existing tools​

With Edge Impulse, you can build AI applications using familiar and well documented methods. The tools in this software suite can be combined to achieve a variety of goals, from detecting anomalies to analyzing signal patterns. They provide several different analysis methods, including signal flattening and analysis of repetitive motion.

The software also allows you to build custom models without coding. There are 3 basic building blocks you must use to build a model. The first one, input block, is used to specify the type of data you want to input to the model. This can be images or time series.

Edge Impulse’s AI platform is available as a free and enterprise version. The free version has some limitations, such as a single developer’s sweat and a cloud storage limit of four GB. The enterprise version, which costs $149 per project, removes these restrictions and allows for up to five users per project.

Edge Impulse enables the development of enterprise-grade ML algorithms that train on real sensor data. These models can be quantised and optimised. Then, they can be deployed on BrainChip Akida devices. Enterprise developers can also leverage the BrainChip MetaTF model deployment block to deploy neuromorphic models.

Edge Impulse is free and easy to use. It helps speed up data pre-processing and model building. It features a user-friendly UI that guides you through the process and allows you to customize your model. It also provides a TensorFlow-lite model library that supports all popular formats.

Edge Impulse’s AI technology is based on the BrainChip Akida processor, a breakthrough neural networking processor architecture that delivers high performance and ultra-low power, while still allowing for on-chip learning. It also enables you to visualize the results of your inference using any web browser.

It competes with startups​

Edge Impulse is a startup that uses machine learning to build smarter embedded devices. The company launched in mid-2019, and has almost 30,000 developers using its platform. Its customers include NASA, Polycom, and Advantech. In a recent funding round, Edge Impulse raised $34 million from investors including Coatue, Momenta Ventures, and Acrew Capital.

The startup uses off-the-shelf machine learning frameworks such as TensorFlow to make its models as easy to use as possible. It also provides tools for domain experts to collect data, classify it, and predict the future. Those features are also available in the free tier of Edge Impulse. The company also offers a subscription option that allows customers to gain access to features like collaboration between multiple engineers, larger datasets, and model versioning.

Edge Impulse’s platform makes it easier to build smarter IoT applications. It supports sensor, audio, and computer vision applications. It can also help with asset tracking and health applications. In addition, it ingests 99 percent of critical sensor data, which improves the performance of its algorithms. This technology also enables developers to quickly and easily create new applications.

Edge Impulse has recently raised $34 million in Series B funding. This investment will allow the startup to expand its operations, marketing, and staff. The company also plans to double its annual recurring revenue and triple its market valuation by 2022. Its current investors include Coatue, Sequoia Capital, and Accel.

As a SaaS platform, the company offers developers a solution to implement TinyML in their enterprise environments. Its SaaS platform includes the entire set of steps that is necessary to build models: data collection, signal processing, and deployment to a sensor. It’s available for free to individual developers, as well as a paid service for enterprise customers.

Great join dot, first I’ve seen of this

 

[Diogenese]

Has we discusse about 'innatera'

From the untrained eye, they are trying to do what Brainchip is doing. Although NONE of their patients is has been grant. Our more knowledgeable shareholders can investigate.

Innatera | Ultra low power intelligence for the sensor edge.

www.innatera.com

Google Patents

Search and read the full text of patents from around the world with Google Patents, and find prior art in our index of non-patent literature.

patents.google.com

Learning

Innatera uses "analog-mixed signal computing" and is designed for time series data (speech, vibration, etc).

What "analog-mixed signal computing" means is not clear from their patents:

WO2022073946A1 ADAPTATION OF SNNS THROUGH TRANSIENT SYNCHRONY

[00144] The SNN core may employ a mixed analog-digital computational platform, i.e. the spike trains incorporate analog information in the timing of the events, which are subsequently transformed back into an analog representation at the inputs of the synaptic matrix.


https://www.eetimes.com/innatera-unveils-neuromorphic-ai-chip-to-accelerate-spiking-networks/

Most other companies working on spiking neural network algorithms and hardware (for example, Prophesee) are targeting images and video streams. Innatera has decided to focus on audio (sound and speech recognition), health (vital signs monitoring) and radar (for consumer/IoT use cases such as elderly person fall sensors which maintain privacy).

Marco Jacobs (Image: Innatera)
“These sensors have time series data, instead of images which are very parallel,” said Marco Jacobs, Innatera VP marketing and business development, in an interview with EE Times. “Our array is especially good at processing time series data… it’s a good technology fit. Also, from a market perspective, we see a lot of interesting applications in this area and not that many solutions that address it.”

(Not altogether sure they are up to speed on what Prophesee does? ... but then why should they be because their analog-mixed signal processor does not work with image data?)