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Proga

Regular
Something to ponder:

When the EQXX was released in January it was stated that the whole production process was achieved in 18 months. If we assume it was finished in December, 2021 then they sat down to start designing EQXX at the end of June, 2020.

Now at some point before then Mercedes as a corporate entity would have had to consider a proposal and agree the budget and corporate direction. We now know that the EQXX is the template for an all electric vehicle future by 2038.

When you read the following article in September, 2019 Mercedes was declaring that they could not envisage a time when Mercedes would not be producing internal combustion engines.

So what happened in the eight months or so from beginning October, 2019 until end of June, 2020 to completely cause Mercedes to reinvent its future and decide it could leapfrog Tesla and produce the first even 1,000 kilometre EV???

Something must have happened or someone came up with an amazing proposal that convinced Mercedes that they had the Edge and could dominate in the EV race.


My opinion only DYOR
FF

AKIDA BALLISTA
New EU legislation to strengthen CO2 emissions standards for new passenger cars and light commercial vehicles in 2020 I think caused it. Basically they saw the writing on the wall. They all have. When the tariffs kick in, it becomes ugly. I think Volvo will only make fully electric vehicles from 2030.
 
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Something to ponder:

When the EQXX was released in January it was stated that the whole production process was achieved in 18 months. If we assume it was finished in December, 2021 then they sat down to start designing EQXX at the end of June, 2020.

Now at some point before then Mercedes as a corporate entity would have had to consider a proposal and agree the budget and corporate direction. We now know that the EQXX is the template for an all electric vehicle future by 2038.

When you read the following article in September, 2019 Mercedes was declaring that they could not envisage a time when Mercedes would not be producing internal combustion engines.

So what happened in the eight months or so from beginning October, 2019 until end of June, 2020 to completely cause Mercedes to reinvent its future and decide it could leapfrog Tesla and produce the first even 1,000 kilometre EV???

Something must have happened or someone came up with an amazing proposal that convinced Mercedes that they had the Edge and could dominate in the EV race.


My opinion only DYOR
FF

AKIDA BALLISTA
In the spider web that is from time to time visible I currently have two crossing points aligning that may be real or imagined.

In 2019 Peter van der Made stated that 100 AKD1000 could undertake all of the computing for an unconnected autonomous vehicle.

Early 2020 the then CEO Mr. Dinardo went to great lengths in a webinar to hose down the idea that AKD1000 would be used in this way. He stated that Peter van der Made gets carried away, that 100 AKD1000 could do this but this was not the focus so stop talking about it and asking questions was the point.

At the time I thought well that is fair enough he wants to focus on the Edge but the idea that 100 AKD1000 could power an unconnected AV is a pretty good statement or example of just how powerful and revolutionary it is and after all your making it so 64 AKD1000 chips can be ganged together.

Anyway as I said points of connection real or imagined that might fit with Mercedes.

My opinion only DYOR
FF

AKIDA BALLISTA
 
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New EU legislation to strengthen CO2 emissions standards for new passenger cars and light commercial vehicles in 2020 I think caused it. Basically they saw the writing on the wall. They all have. When the tariffs kick in, it becomes ugly. I think Volvo will only make fully electric vehicles from 2030.
That explains EV but it does not explain EQXX leaping from nowhere onto the drawing board from a position where Mercedes were saying we will keep doing our ICE and building a few token EV models.

My opinion only DYOR
FF

AKIDA BALLISTA
 
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Subtle... Renesas has recently liked a comment from one of our Chippers. The comment was "Akida Ballista" which was mentioned on the below white paper from Renesas!

Well done Nick!
1648127238232.png
 
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Esq.111

Fascinatingly Intuitive.
Morning Zeeb0t,

Thanks for getting the platform up and running again.

Was this outage due to nefarious actors or standard running updates etc.

Thanks once again ,
Top job.

Regards,
Esq.
 
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zeeb0t

Administrator
Staff member
Morning Zeeb0t,

Thanks for getting the platform up and running again.

Was this outage due to nefarious actors or standard running updates etc.

Thanks once again ,
Top job.

Regards,
Esq.

It was definitely not nefarious actors. It was due to a standard server update which has somehow damaged a server software version. Being a Linux based server these things typically just manage themselves, however, this one failed. I rolled back the software update and it’s now back online. I’m holding back the update until the vendor can comment on it.

Nothing to worry about :)
 
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zeeb0t

Administrator
Staff member
Morning Zeeb0t,

Thanks for getting the platform up and running again.

Was this outage due to nefarious actors or standard running updates etc.

Thanks once again ,
Top job.

Regards,
Esq.

p.s., it took about 2 hours to resolve as the first hour was spent still getting my beauty sleep in. Once I realised there was an issue, I got out of bed, fixed a coffee, then fixed the server :p
 
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Esq.111

Fascinatingly Intuitive.
It was definitely not nefarious actors. It was due to a standard server update which has somehow damaged a server software version. Being a Linux based server these things typically just manage themselves, however, this one failed. I rolled back the software update and it’s now back online. I’m holding back the update until the vendor can comment on it.

Nothing to worry about :)
Zeeb0t,

Good to hear.

I had to reload this Web address from one of your comments on the BRN twitter site, to get my phone working properly again.

All good.

Thanks mate.


Esq.
 
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zeeb0t

Administrator
Staff member
Zeeb0t,

Good to hear.

I had to reload this Web address from one of your comments on the BRN twitter site, to get my phone working properly again.

All good.

Thanks mate.


Esq.

Yes unfortunately devices may cache the site as still down. You could try refresh the cache by putting something random on the end... e.g., maybe visiting https://thestockexchange.com.au/?x=123 so your browser tries to get a fresh copy instead of relying on a blank cache.
 
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Dang Son

Regular
Yea I’d be buying that
Hi, the free microsoft Win10 calculator tool goes to 9.999999999999999 Quadrillion
1,000,000,000,000,000 (one quadrillion) is an even sixteen-digits composite number following 999999999999999 and preceding 1000000000000001.
This calculator has the convenience of a currency convertor also plus more
 
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butcherano

Regular
Subtle... Renesas has recently liked a comment from one of our Chippers. The comment was "Akida Ballista" which was mentioned on the below white paper from Renesas!

Well done Nick!
View attachment 3099
The reactions option on Linkedin is blank when I click on it to see who gave the thumbs up.

Link to the post here...


But I don't believe this is Akida. DRP-AI is the Renesas technology that they have were working on since well before they teamed up with Brainchip. This uses the the old school AI accelerator method (multiply and accumulate).

I thought that the Renesas DRP-AI and AI-MAC is what Akida was intended to replace or improve? Would be keen to hear other people's thoughts on this though.

1648159510079.png
 
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JK200SX

Regular
The reactions option on Linkedin is blank when I click on it to see who gave the thumbs up.

Link to the post here...


But I don't believe this is Akida. DRP-AI is the Renesas technology that they have were working on since well before they teamed up with Brainchip. This uses the the old school AI accelerator method (multiply and accumulate).

I thought that the Renesas DRP-AI and AI-MAC is what Akida was intended to replace or improve? Would be keen to hear other people's thoughts on this though.

View attachment 3108
Renesas Electronics gave a like to the reply post.
 
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The reactions option on Linkedin is blank when I click on it to see who gave the thumbs up.

Link to the post here...


But I don't believe this is Akida. DRP-AI is the Renesas technology that they have were working on since well before they teamed up with Brainchip. This uses the the old school AI accelerator method (multiply and accumulate).

I thought that the Renesas DRP-AI and AI-MAC is what Akida was intended to replace or improve? Would be keen to hear other people's thoughts on this though.

View attachment 3108
G'day Butch, BTW mate love your work, keep it up.

I believe you're correct, this is not Brainchip on the basis DRP is proprietary to Renesas. However, I thought it was interesting Renesas giving a subtle nod of acknowledgement... almost like saying, 'we hear you, we're working on it, patience'.

Mid-22 they're due to release their chip with Akida IP, right?
 
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butcherano

Regular
G'day Butch, BTW mate love your work, keep it up.

I believe you're correct, this is not Brainchip on the basis DRP is proprietary to Renesas. However, I thought it was interesting Renesas giving a subtle nod of acknowledgement... almost like saying, 'we hear you, we're working on it, patience'.

Mid-22 they're due to release their chip with Akida IP, right?
Cheers @DaBenjamins....yeah that's exactly what I was thinking. Although it's odd that I can't actually see who gave the thumbs up. This is what I get when I click on the icon for Nick's post....just a blank box... Not sure if this is just me.

@IndepthDiver put an awesome post on the Renesas thread pointing towards the direction of Akida being used in the RA8 microcontroller which is due for release this year. That made heaps of sense to me. Link to his post here.

1648162207285.png
 
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The reactions option on Linkedin is blank when I click on it to see who gave the thumbs up.

Link to the post here...


But I don't believe this is Akida. DRP-AI is the Renesas technology that they have were working on since well before they teamed up with Brainchip. This uses the the old school AI accelerator method (multiply and accumulate).

I thought that the Renesas DRP-AI and AI-MAC is what Akida was intended to replace or improve? Would be keen to hear other people's thoughts on this though.

View attachment 3108
Hi Butch,

I don’t think there will be any guessing when the Renasas releases a product with Akida inside.

I am expecting an exciting promotion ushering in a new wave of intelligent sensors and the benefits which Akida offers.

Akida will be a selling point!

Cheers!
 
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Dozzaman1977

Regular
Our CEO Sean said recently in a interview that "Renasas are Developing a SET of products, and as they start shipping those products we will collect royalties on these"

SET = a collection of well defined objects (or in this case multiple products IMO)
Money GIF
 
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On the theme of Renesas and implementing AKIDA technology in MCU’s I have been excited by this prospect since Peter van der Made and Anil Mankar separately disclosed this was where Renesas was heading. I have lacked the depth of technical knowledge to bring a proper explanation of why this is so exciting. Just now on the Renesas thread I found the following.

This article is not about AKIDA technology but It identifies the opportunity to be had in making trillions of MCUs smart and offering a clever but convoluted old school solution.

If you had a calculator that could do trillions you could work out what 1 trillion times a 2 cent royalty would be for IP to make an MCU smart with every cent being profit to the owner of the IP:

Date 02/18/20

What is tinyML?​

What is tinyML technology?​

“We see a new world with trillions of intelligent devices enabled by tinyML technologies that sense, analyze, and autonomously act together to create a healthier and more sustainable environment for all.”

This is a quote by Evgeni Gousev, senior director at Qualcomm and co-chair of the tinyML Foundation, in his opening remarks at a recent conference.

Machine learning is a subset of artificial intelligence. tinyML aka tiny ml is an abbreviation for tiny machine learning and means that machine learning algorithms are processed locally on embedded devices.

TinyML is very similar with Edge AI, but tinyML takes Edge AI one step further, making it possible to run machine learning models on the smallest microcontrollers (MCU’s). Learn more about Edge AI here.

What is a microcontroller?​

507_o_microcontroller-stm32-based-on-arm.png

Embedded systems normally include a microcontroller. A microcontroller is a compact integrated circuit designed to govern a specific operation in an embedded system. A typical microcontroller includes a processor, memory and input/output (I/O) peripherals on a single chip.
Microcontrollers are cheap, with average sales prices reaching under $0.50, and they’re everywhere, embedded in consumer and industrial devices. At the same time, they don’t have the resources found in generic computing devices. Most of them don’t have an operating system.
They have a small CPU, are limited to a few hundred kilobytes of low-power memory (SRAM) and a few megabytes of storage, and don’t have any networking gear. They mostly don’t have a mains electricity source and must run on cell and coin batteries for years.

What are the advantages with tinyML?​

There are a number of advantages with running tinyML machine learning models on embedded devices.

Low Latency: Since the machine learning model runs on the edge, the data doesn't have to be sent to a server to run inference. This reduces the latency of the output.
Low Power Consumption: Microcontrollers are ultra low power which means that they consume very little power. This enables them to run without being charged for a really long time.
Low Bandwidth: As the data doesn’t have to be sent to the server constantly, less internet bandwidth is used.
Privacy: Since the machine learning model is running on the edge, the data is not stored in the cloud.

What are the challenges with tinyML?​

Deep learning models require a lot of memory and consumes a lot of power. There have been multiple efforts to shrink deep machine learning models to a size that fits on tiny devices and embedded systems. Most of these efforts are focused on reducing the number of parameters in the deep learning model. For example, “pruning,” a popular class of optimization algorithms, compress neural networks by removing the parameters that are less significant in the model’s output.
The problem with pruning methods is that they don’t address the memory bottleneck of the neural networks. Standard implementations of embedded machine learning require an entire network layer and activation maps to be loaded into memory. Unfortunately, classic optimization methods don’t make any big changes to the early layers of the network, especially in convolutional networks.

What is quantization?​

Quantization is a branch of computer science and data science, and has to do with the actual implementation of the neural network on a digital computer, eg tiny devices. Conceptually we can think of it as approximating a continuous function with a discrete one.
Depending on the bit width of chosen integers (64, 32, 16 or 8 bits) and the implementation this can be done with more or less quantization errors. All modern PCs, smartphones, tablets, and more powerful microcontrollers (MCUs) for embedded systems have a so-called floating-point unit or an FPU.
This is a piece of hardware next to or is integrated with the main processor, with the purpose to make floating-point operations fast. But for the tiniest MCUs there are no FPUs and instead, one must resort to either of two options: Transform the data to integer numbers and perform all the calculations with integer arithmetic or perform the floating-point calculations in software.
The latter approach takes considerably more processor time, so go get a good performance on these chips (e.g., ARM’s M0-M3 cores), the former approach is the preferred one. If memory is sparse, which usually is the case on these devices, one can specify the integers to be 16 or 8-bit, thereby reducing the RAM and Flash memory used by the program to half, or a quarter.

What is tinyML software?​

tinyML software are typically ultra low power tinyML applications that run on a physical hardware device. There are a large number of machine learning algorithms that are used, but the trend is that deep learning is becoming more and more popular. People that develop tinyML applications are normally data scientists, machine learning engineers or embedded developers. Normally the tinyML models are developed and trained in a cloud system. When the tinyML application runs on the hardware device, the tinyML model can then understand what it was trained for. This is called inference.

What is tinyML hardware?​

tinyML can run on a range of different hardware platforms from Arm Cortex M-series processors to advanced neural processing devices. Deep learning normally requires more powerful hardware platforms than other types of machine learning algorithms. IoT devices are an example of tinyML hardware devices. Many people are using an Arduino board to demonstrate machine learning applications.

What is a tinyML framework?​

A tinyML framework is a software platform that makes it easier for developers to develop tinyML applications. Tensorflow Lite for microcontrollers is one of the most popular embedded machine learning frameworks.

What is a tinyML development platform?​

A tinyML development platform is an end-to-end software platform, where users can develop tinyML applications. They start with collecting data, they can use AutoML to develop the machine learning models and eventually they can deploy the machine learning model on a tiny device. The main benefit with a tinyML platform is that it reduces time-to-market and lower the costs for the developer.

What are some examples of tinyML use cases?​

The benefits of tinyML are huge, and the number of use cases for tinyML technology are almost unlimited. Popular use cases include Computer vision, visual wake words, keyword spotters, predictive maintenance, gesture recognition, maintenance of industrial machines and many more.

Imagimob in tinyML​

Imagimob offers Imagimob AI which is a tinyML development platform that covers the end-to-end process from data collection to deploying AI models on a microcontroller board”

My opinion only DYOR
FF

AKIDA BALLISTA
 
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Yak52

Regular
Did YOU have your Stop Loss Triggered this morning ????

B0T STOP LOSS TRIGGERED.jpg


Yak52
 
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Did YOU have your Stop Loss Triggered this morning ????

View attachment 3121

Yak52
Hi Yak

Do you think you should be watching the bots before you get the results next week. 😂

Stay calm we need you here long term.
FF
 
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Slade

Top 20
The reactions option on Linkedin is blank when I click on it to see who gave the thumbs up.

Link to the post here...


But I don't believe this is Akida. DRP-AI is the Renesas technology that they have were working on since well before they teamed up with Brainchip. This uses the the old school AI accelerator method (multiply and accumulate).

I thought that the Renesas DRP-AI and AI-MAC is what Akida was intended to replace or improve? Would be keen to hear other people's thoughts on this though.

View attachment 3108
Hi Butcherano, I think that Akida could be used to improve DRP-AI. The R in DRP stands for re-configurable, and the system was made to be flexible so that it could be updated and customized with new AI technology. Of course I could be wrong and it turns out DRP-AI is not flexible enough to accommodate Akida IP. Someone with greater tech knowledge will need to read the white paper and comment.
 
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