FWIW. A young Ferdinand Porsche exhibited a hybrid Austro Daimler car at the 1900 World Exposition in Paris. It had electric motors on the front wheels and an internal combustion engine on the rear. ( Truly ahead of his time.)And an old beautiful realization that should be unknown to many here. Unfortunately it is impossible for me to make my own better photo of this magazine. Only speed records from 1898 with the beginning and EVs. How the story went you know.
View attachment 32745
Elektrowagen means EV
It began with EVs
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This reduced in size article is from Sally Ward EE times .
Embedded World 2023
Also on the STMicro booth were another couple of fun demos, including a washing machine that could tell how much laundry was in the machine in order to optimize the amount of water added. This system is sensorless; it is based on AI analysis of the current required to drive the motor, and predicted the weight of the 800g laundry load to within 30g. A robot vacuum cleaner equipped with a time-of-flight sensor also used AI to tell what type of floor surface it was cleaning, to allow it to select the appropriate cleaning method.
The M85 is a larger core than the Cortex-M55, but both are equipped with Helium—Arm’s vector extensions for the Cortex-M series—ideal for accelerating ML applications. Renesas’ figures had the M85 running inference 5.3× faster than a Renesas M7-based design, though the M85 was also running faster (480 MHz compared with 280).
Renesas’ demo had Plumerai’s person-detection model up and running in 77 ms per inference.
Renesas’ not-yet-announced Cortex-M85 device is the first we’ve seen running AI on the M85. Shown here running Plumerai people-detection model. (Source: EE Times/Sally Ward-Foxton)
Renesas field application engineer Stefan Ungerechts also gave EE Times an overview of the DRP-AI (dynamically reconfigurable processor for AI), Renesas’ IP for AI acceleration. A demo of the RZ/V2L device, equipped with a 0.5 TOPS @ FP16 (576 MACs) DRP-AI engine, was running tinyYOLOv2 in 27 ms at 500 mW (1 TOPS/W). This level of power efficiency means no heat sink is required, Ungerechts said.
The DRP-AI is, in fact, a two-part accelerator; the dynamically reconfigurable processor handles acceleration of non-linear functions, then there is a MAC array alongside it. Non-linear functions in this case might be image preprocessing functions or model pooling layers of a neural network. While the DRP is reconfigurable hardware, it is not an FPGA, Ungerechts said. The combination is optimized for feed-forward networks like convolutional neural networks commonly found in computer vision, and Renesas’ software stack allows either the whole AI workload to be passed to the DRP-AI or use of a combination of the DRP-AI and the CPU.
Also available with a DRP-AI engine are the RZ/V2MA and RZ/V2M, which offer 0.7 TOPS @ FP16 (they run faster than the -V2L at 630 MHz compared to 400, and have higher memory bandwidth).
A next-generation version of the DRP-AI that supports INT8 for greater throughput, and is scaled up to 4K MACs, will be available next year, Ungerechts said.
Squint CEO Kenneth Wenger told EE Times that the company wants to increase trust in AI decision making for applications like autonomous vehicles (AVs), healthcare and fintech. The company takes pre-production models and tests them for weaknesses—identifying in what situations they are more likely to make a mistake.
This information can be used to set up a mitigating factors, which might include human-in-the-loop—perhaps flagging a medical image to a doctor—or trigger a second, more specialized model that has been specifically trained for that situation. Squint’s techniques can also be used to tackle “data drift”—for maintaining models over longer periods of time.
Embedl has also been a part of the VEDL-IoT project, an EU-funded project in collaboration with Bielefeld University that aims to develop an IoT platform, which distributes AI across a heterogeneous cluster.
Their demo showed managing AI workloads across different hardware: an Nvidia AGX Xavier GPU in a 5G basestation and an NXP i.MX8 application processor in a car. With sufficient 5G bandwidth available, “difficult” layers of the neural network could be computed remotely in the basestation, and the rest in the car, for optimum latency. Reduce the 5G bandwidth available, and more or all of the workload goes to the i.MX8. Embedl had optimized the same model for both hardware types.
The VEDL-IoT project demo shows splitting AI workloads across 5G infrastructure and embedded hardware. (Source: EE Times/Sally Ward-Foxton)
A separate wake word demo ran in 50 ms on the xG24’s accelerator, and a third board was running a gesture recognition algorithm.
Embedded World 2023
Also on the STMicro booth were another couple of fun demos, including a washing machine that could tell how much laundry was in the machine in order to optimize the amount of water added. This system is sensorless; it is based on AI analysis of the current required to drive the motor, and predicted the weight of the 800g laundry load to within 30g. A robot vacuum cleaner equipped with a time-of-flight sensor also used AI to tell what type of floor surface it was cleaning, to allow it to select the appropriate cleaning method.
Renesas
Next stop was the Renesas booth, to see the Arm Cortex-M85 up and running in a not-yet-announced product (due to launch in June). This is the first time EE Times has seen AI running on a Cortex-M85 core, which was announced by Arm a year ago.The M85 is a larger core than the Cortex-M55, but both are equipped with Helium—Arm’s vector extensions for the Cortex-M series—ideal for accelerating ML applications. Renesas’ figures had the M85 running inference 5.3× faster than a Renesas M7-based design, though the M85 was also running faster (480 MHz compared with 280).
Renesas’ demo had Plumerai’s person-detection model up and running in 77 ms per inference.
Renesas’ not-yet-announced Cortex-M85 device is the first we’ve seen running AI on the M85. Shown here running Plumerai people-detection model. (Source: EE Times/Sally Ward-Foxton)Renesas field application engineer Stefan Ungerechts also gave EE Times an overview of the DRP-AI (dynamically reconfigurable processor for AI), Renesas’ IP for AI acceleration. A demo of the RZ/V2L device, equipped with a 0.5 TOPS @ FP16 (576 MACs) DRP-AI engine, was running tinyYOLOv2 in 27 ms at 500 mW (1 TOPS/W). This level of power efficiency means no heat sink is required, Ungerechts said.
The DRP-AI is, in fact, a two-part accelerator; the dynamically reconfigurable processor handles acceleration of non-linear functions, then there is a MAC array alongside it. Non-linear functions in this case might be image preprocessing functions or model pooling layers of a neural network. While the DRP is reconfigurable hardware, it is not an FPGA, Ungerechts said. The combination is optimized for feed-forward networks like convolutional neural networks commonly found in computer vision, and Renesas’ software stack allows either the whole AI workload to be passed to the DRP-AI or use of a combination of the DRP-AI and the CPU.
Also available with a DRP-AI engine are the RZ/V2MA and RZ/V2M, which offer 0.7 TOPS @ FP16 (they run faster than the -V2L at 630 MHz compared to 400, and have higher memory bandwidth).
A next-generation version of the DRP-AI that supports INT8 for greater throughput, and is scaled up to 4K MACs, will be available next year, Ungerechts said.
Squint
Squint, an AI company launched earlier this year, is taking on the challenge of explainable AI.Squint CEO Kenneth Wenger told EE Times that the company wants to increase trust in AI decision making for applications like autonomous vehicles (AVs), healthcare and fintech. The company takes pre-production models and tests them for weaknesses—identifying in what situations they are more likely to make a mistake.
This information can be used to set up a mitigating factors, which might include human-in-the-loop—perhaps flagging a medical image to a doctor—or trigger a second, more specialized model that has been specifically trained for that situation. Squint’s techniques can also be used to tackle “data drift”—for maintaining models over longer periods of time.
Embedl
Swedish AI company Embedl is working on retraining models to optimize them for specific hardware targets. The company has a Python SDK that fits into the training pipeline. Techniques include replacing operators with alternatives that may run more efficiently on the particular target hardware, as well as quantization-aware retraining. The company’s customers so far have included automotive OEMs and tier 1s, but they are expanding to Internet of Things (IoT) applications.Embedl has also been a part of the VEDL-IoT project, an EU-funded project in collaboration with Bielefeld University that aims to develop an IoT platform, which distributes AI across a heterogeneous cluster.
Their demo showed managing AI workloads across different hardware: an Nvidia AGX Xavier GPU in a 5G basestation and an NXP i.MX8 application processor in a car. With sufficient 5G bandwidth available, “difficult” layers of the neural network could be computed remotely in the basestation, and the rest in the car, for optimum latency. Reduce the 5G bandwidth available, and more or all of the workload goes to the i.MX8. Embedl had optimized the same model for both hardware types.
The VEDL-IoT project demo shows splitting AI workloads across 5G infrastructure and embedded hardware. (Source: EE Times/Sally Ward-Foxton)Silicon Labs
Silicon Labs had several xG24 dev kits running AI applications. One had a simple Sparkfun camera with the xG24 running people counting, and calculating the direction and speed of movement.A separate wake word demo ran in 50 ms on the xG24’s accelerator, and a third board was running a gesture recognition algorithm.
BrainChip
BrainChip had demos running on a number of partner booths, including Arm and Edge Impulse. Edge Impulse’s demo showed the company’s FOMO (faster objects, more objects) object detection network running on a BrainChip Akida AKD1000 in under 1 mW.Investig8ed
Regular
At a guess I would say that they may have received permission to use akida for their help in tapping out akida 1000 and would only pay a royalty on chips produced. Who the hell knows what deals are being done.
Investig8ed
Regular
Look into the EQA with the new MBOSSo what’s the consensus on if Mercedes Benz are going to have vehicles containing akida in 2024? I’ve read a few reports now that it “may” contain the tech that was used in the EQXX (akida)
Mercedes-Benz EV to ditch EQ branding and take a shot at the Model 3
Electrification is now advancing at a breakneck pace, and it is becoming increasingly apparent that who remain indecisive between jumping onto the e-mobility technology or to keep producing fossil fuelled vehicles and risk being left behind.www.britanniacarleasing.co.uk
“Mercedes-Benz have also been eager to reveal that this new electric saloon will be the pioneer of the company’s proprietary MB Operating System, which will be used instead of the Google connectivity options integrated into the vehicles of competitors such as Volvo. This homemade infotainment system may even feature the experimental processor being developed by Mercedes that executes tasks in “neuromorphic spikes”, which essentially translates to the processor steadily accumulating tasks until a certain quantity has been reached, when they are all carried out simultaneously”
Fact Finder
Top 20
AKIDA 2nd Generation
“Availability:
Engaging with lead adopters now.
General availability in Q3’2023”
brainchip.com
WHO ARE THE LEAD ADOPTERS???
My opinion only DYOR
FF
AKIDA BALLISTA
“Availability:
Engaging with lead adopters now.
General availability in Q3’2023”
Akida Generations
Explore cutting-edge technology, advanced neural networks, and unparalleled performance. Unleash the power of akida™ NOW!
brainchip.com
WHO ARE THE LEAD ADOPTERS???
My opinion only DYOR
FF
AKIDA BALLISTA
cosors
👀
I have to find out and understand that first. I post it as a lover of TSE and as someone who takes the rules seriously. I have a hunch, but I have to look first. So I'm posting this for transparency and because we all need to learn:
I still haven't figured out which of my posts was worth deleting for Dreddbot. But I have cleaned up some more myself. I fear that Dreddb0t has exaggerated especially when I look at a lot of the posts here. A lot of joy, fun and laughter would be sacrificed to the clean factual thread. By logic, this post would also have to be deleted and my avatar banned. I don't know if I'd like that. Imagine every post that is off topic would be deleted.
I am the law. I will also think about that for the time being.
____
I still haven't figured out which of my posts was worth deleting for Dreddbot. But I have cleaned up some more myself. I fear that Dreddb0t has exaggerated especially when I look at a lot of the posts here. A lot of joy, fun and laughter would be sacrificed to the clean factual thread. By logic, this post would also have to be deleted and my avatar banned. I don't know if I'd like that. Imagine every post that is off topic would be deleted.
I am the law. I will also think about that for the time being.
____
Last edited:
chapman89
Founding Member
Intellisense-
brainchip.com
BrainChip’s Neuromorphic Technology Enables Intellisense Systems to Address Needs for Next-Generation Cognitive Radio Solutions
Discover the transformative capabilities of BrainChip's advanced AI technology in enabling efficient and intelligent radio communications.
brainchip.com
What years? I was there in 91... FHS
GStocks123
Regular
BrainChip on LinkedIn: BrainChip’s Neuromorphic Technology Enables Intellisense Systems to…
BrainChip’s Neuromorphic Technology Enables Intellisense Systems to Address Needs for Next-Generation Cognitive Radio Solutions https://lnkd.in/gkPUYJtA
Dhm
Regular
@Steve10 when Qualcomm revealed their Snapdragon with Prophesee at CES, the blur free photography contribution was not from Brainchip even though many of us thought it was. Therefore it must have been Synsense. Round one to Synsense although there are many rounds to play out over coming years.
And this from Bill Gates…..
The next frontiers
There will be an explosion of companies working on new uses of AI as well as ways to improve the technology itself. For example, companies are developing new chips that will provide the massive amounts of processing power needed for artificial intelligence. Some use optical switches—lasers, essentially—to reduce their energy consumption and lower the manufacturing cost. Ideally, innovative chips will allow you to run an AI on your own device, rather than in the cloud, as you have to do today.
TechGirl
Founding Member
Intellisense-
BrainChip’s Neuromorphic Technology Enables Intellisense Systems to Address Needs for Next-Generation Cognitive Radio Solutions
Discover the transformative capabilities of BrainChip's advanced AI technology in enabling efficient and intelligent radio communications.
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Holy farken ShitBalls
They are no longer only EAP’s they are Partners
Thanks Jesse
Fact Finder
Top 20
This is potentially huge. I have rabbited on about the cognitive communications market and how important it is to Brainchip’s commercial success.Intellisense-
BrainChip’s Neuromorphic Technology Enables Intellisense Systems to Address Needs for Next-Generation Cognitive Radio Solutions
Discover the transformative capabilities of BrainChip's advanced AI technology in enabling efficient and intelligent radio communications.
View attachment 32749
And before anyone flogs the dead horse about this is only NASA etc; read this:
The global radio frequency front end market size was valued at $18.8 billion in 2021, and is projected to reach $69.9 billion by 2031, growing at a CAGR of 13.8% from 2022 to 2031.
https://www.alliedmarketresearch.com › ...
Radio Frequency Front End Market Size and Forecast
My opinion only DYOR
FF
AKIDA BALLISTA
I was born in Forbes
wooooooooooooooooow!Intellisense-
BrainChip’s Neuromorphic Technology Enables Intellisense Systems to Address Needs for Next-Generation Cognitive Radio Solutions
Discover the transformative capabilities of BrainChip's advanced AI technology in enabling efficient and intelligent radio communications.
View attachment 32749
TechGirl
Founding Member
And they mentioned for “commercial and government markets”
That doesn’t sound like just for NASA
“Intellisense Systems Inc. has selected its neuromorphic technology to improve the cognitive communication capabilities on size, weight and power (SWaP) constrained platforms (such as spacecraft and robotics) for commercial and government markets.”
Fact Finder
Top 20
No it does not have to be SynSense. Prophesee can run their sensor using Von Neumann computing but it is less efficient. So just because Prophesee is not using AKIDA does not immediately default to SynSense far from it.
What Prophesee has found in Brainchip AKIDA is the optimum processing solution for their technology.
My opinion only DYOR
FF
AKIDA BALLISTA
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