BRN Discussion Ongoing

[Sirod69]

embedded news: Arm and CEVA ship record chips, SOMs, and more​

August 12, 2022 Nitin Dahad
This week’s embedded news features various announcements around system on modules (SOMs), as well as news from Arm and CEVA on their chip shipment successes, plus more from the industry.

Arm this week reported record quarterly royalty revenue, exceeding $400 million for the first time, at $453 million for Q1 of FY 2022. It announced more record figures too, with a record Q1 total revenue of $719 million, and a record number of Q1 unit shipments, with its partners shipping 7.4 billion Arm-based chips in Q1. Arm said it has now achieved four quarters of more than 7 billion Arm-based chips shipped.

embedded news: Arm & CEVA shipping record chips, SOMs

A roundup of the latest embedded news including new SOMs, plus news from Arm and CEVA on their chip shipment successes.

www.embedded.com

 

[Sirod69]

Autonomous Vehicle Deployment: pick up, drop off, and great user experience​

Here is a sample of the companies who’ve entered the commercial arena with driverless cars:

• Easymile operates autonomous shuttles across the globe, performing thousands of commercial drives.
• Cruise has been approved to deploy commercial driverless cars in San Francisco through its AV ride-sharing service.
• Argo-AI has begun piloting autonomous taxis, sans safety drivers, in Miami, Florida, and Austin, Texas.
• Meanwhile, in China, AutoX robotaxis are operating without them as well.
• Gatik’s delivery trucks have been conducting driverless hauls successfully for some time without human backup drivers.

Autonomous Vehicle Deployment - pick up, drop off

Autonomous vehicle large-scale deployment requires great users experience even during parking and drop off

driveu.auto

While the delivery-robot market is growing, it’s difficult to picture how an unassisted autonomous bot would find the customer at a beach or park. Even as it drives smoothly to a pinpointed destination, it may pass the actual customer standing nearby, or, say, waving to the robot while yelling, “over here!”

 

[Iseki]

Hi all,

Non Brainchip question so feel free to skip over it:

I can finally move a portion of my Super into a SMSF at the end of November. It’s been agonising waiting for this to occur, watching Brainchip and a few others go up whilst unable to put my super into it!

To balance my portfolio I am asking if anyone has any other solid share tips they’d like to share please?

Obviously I’m going to do my own research which is why I’m asking now as it give me a few months to consider my options and investment strategy.

Rather than clog up this thread if you could message me with any advice/share tips it would be much appreciated.

Thanks in advance.

IMU - to cure cancer by shrinking tumors
PAB - to cure cancer by stopping it spreading
ACW - to cure Alzheimers
CGS - to diagnose Alzheimers using AI in a phone
PLY - Computer games where you play for crypto
4DS - ReRam currently in TH
WBT - ReRam

Spec stocks only. Not advice, obviously. You need to know your risk profile, set stop losses. Good luck!

 

[Food4 thought]

Only speculating but I think we know which ARM is powered by Akida


View attachment 14077

Yes of course
Clearly it’s the skinny one
It doesn’t need that much power but it packs a mighty good punch

 

[Deadpool]

What a boring bunch you are today, just remind me not to come to your funeral when ya dead.

Well it was 32

View attachment 14079

mate, I see your still at the Albatross

 

[stuart888]

What a boring bunch you are today, just remind me not to come to your funeral when ya dead.

Well it was 32

View attachment 14079

Wow, toilet paper stacks rather than computational graphs.

I always wondered how the smart folks design their neural networks!

 

[stuart888]

View attachment 14060
Looks like you can use 3 types of the ZMOD series for digital gas sensor for a fridge.
They state in the system benefits write up that " Dedicated MCU for AI Based powerful sensor data evaluation". Fingers Crossed !!!!!!

The refrigerator sensor inference has a lot of use cases. I would love for my refrigerator's outside display to let me know that my half-n-half cream for my coffee has gone sour. I would not have to stiff-test it ever again!

Also it can monitor for too-much-bacteria of all forms and warn people before they eat the food. Food born-illness is a big deal for lots of people and the elderly.

Plus, auto restocking, like the Amazon Fridge. Consumers are going to want these products, as anything that helps health is going to fly.

 

[Deleted member 118]

mate, I see your still at the Albatross

How did you know lol

 

[jtardif999]

Great post, as for is Akida too good - I think the CNN to SNN automated conversion Akida offers has allowed it to be readily accepted into the DL community. You can’t argue much with being able to take CNN trained models into the sparsity of SNN and finish up with something that is best in class for SWaP and allows continued improvement via on chip learning. That’s a great selling line imo.

Ah, and has been whispered to me - the best example of Akida never being too good is Renesas only licensing 2 nodes. What other technology in this space can be configured in such a way as to be powerful enough to potentially augment server farm performance - when ganged together with other Akida’s (80 + 80 + .. nodes) or split out into as few as 2 nodes for smarting up an air quality sensor?

 

[jtardif999]

View attachment 14066

I particularly like where it says “the ZMOD.. AI firmware can be implemented on any Renesas micro controller”

 

[Food4 thought]

I just wonder what sort of impact the Climate Bill will have on our little brain chip investment.
It’s a few dollars that they are throwing at this.
We ( ) use less power
Create less heat
Now how can we help save the world
The mind goes into overtime just thinking of all the applications where we can be involved.

 

[Makeme 2020]

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1. Home
2. Diabetes
3. Home
4. Health informatics

---

AUGUST 9, 2022

AI + ECG heart trace can accurately predict diabetes and pre-diabetes​

by British Medical Journal

Credit: CC0 Public Domain
An artificial intelligence (AI) algorithm, derived from the features of individual heartbeats recorded on an ECG (electrocardiogram), can accurately predict diabetes and pre-diabetes, suggests preliminary research published in the online journal BMJ Innovations.

If validated in larger studies, the approach could be used to screen for the disease in low resource settings, say the researchers.
An estimated 463 million adults around the world had diabetes in 2019, and picking up the disease in its early stages is key to preventing subsequent serious health problems. But diagnosis relies heavily on the measurement of blood glucose.
This is not only invasive but also challenging to roll out as a mass screening test in low resource settings, point out the researchers.
Structural and functional changes in the cardiovascular system occur early on even before indicative blood glucose changes, and these show up on an ECG heart trace.
The researchers therefore wanted to see if machine learning (AI) techniques could be used to harness the screening potential of ECG to predict pre-diabetes and type 2 diabetes in people at high risk of the disease.
They drew on participants in the Diabetes in Sindhi Families in Nagpur (DISFIN) study, which looked at the genetic basis of type 2 diabetes and other metabolic traits in Sindhi families at high risk of the disease in Nagpur, India.
Families with at least one known case of type 2 diabetes and living in Nagpur, which has a high density of Sindhi people, were enrolled in the study.
Participants provided details of their personal and family medical histories, their normal diet, and underwent a full range of blood tests and clinical assessments. Their average age was 48 and 61% of them were women.
Pre-diabetes and diabetes were identified from the diagnostic criteria specified by the American Diabetes Association.
The prevalence of both type 2 diabetes and pre-diabetes was high: around 30% and 14%, respectively. And the prevalence of insulin resistance was also high—35%—-as was the prevalence of other influential coexisting conditions—high blood pressure (51%), obesity (around 40%), and disordered blood fats (36%).
A standard 12-lead ECG heart trace lasting 10 seconds was done for each of the 1262 participants included. And 100 unique structural and functional features for each lead were combined for each of the 10,461 single heartbeats recorded to generate a predictive algorithm (DiaBeats).
Based on the shape and size of individual heartbeats, the DiaBeats algorithm quickly detected diabetes and prediabetes with an overall accuracy of 97% and a precision of 97%, irrespective of influential factors, such as age, gender, and coexisting metabolic disorders.
Important ECG features consistently matched the known biological triggers underpinning cardiac changes that are typical of diabetes and pre-diabetes.
The researchers acknowledge that the study participants were all at high risk of diabetes and other metabolic disorders, so unlikely to represent the general population. And DiaBeats was slightly less accurate in those taking prescription meds for diabetes, high blood pressure, high cholesterol, etc.
Nor were data available for those who became pre-diabetic or diabetic, making it impossible to determine the impact of early screening.
"In theory, our study provides a relatively inexpensive, non-invasive, and accurate alternative [to current diagnostic methods] which can be used as a gatekeeper to effectively detect diabetes and pre-diabetes early in its course," they conclude.
"Nevertheless, adoption of this algorithm into routine practice will need robust validation on external, independent datasets," they caution.

 

[Makeme 2020]

Short video from the ASX investor.....

 

[Straw]

Well there is one solution to waiting on announcements.... take a trip to Malta

 

[Boab]

Elon explains why Tesla won't use LiDar. Well worth a watch of the video.

Why Tesla Won’t Use LIDAR

And which technology is ideal for self-driving cars

towardsdatascience.com

 

[mrgds]

Short video from the ASX investor.....

Thnx @Makeme 2020 ,
We have all wondered about "Mr ASX" and his possible presence here on TSEx, and of course his sign off of "Akida Ballista"
when discussing BRN.
Seems to me a very appropriate video clip for the time us holders are experiencing between any positive major announcements.
Im like everyone here, patiently waiting, .............. "pantene comes to mind"

Meanwhile, .......................... "IM STILL FEELIN IT" ( in the hot tub ) .......................... .........................

Akida Ballista

 

[mrgds]

Elon explains why Tesla won't use LiDar. Well worth a watch of the video.

Why Tesla Won’t Use LIDAR

And which technology is ideal for self-driving cars

towardsdatascience.com

Thnx @Boab
Myself, i am really looking forward to Tesla Ai day ( Sept 30 ) , as Musk has indicated some really big news coming out.
Akida powered Optimus ....................

And here we all are thinking Musks recent Tesla share selling ( $9 billion ? ) is for the possible acquisition of Twitter .....

I know where his money would/might be better spent, ................... BUT, ............. he"ll only get a "minority partnership" for that price.

AKIDA ( Akimus ) BALLISTA

 

[equanimous]

AI platform market is anticipated to be valued at $254bn by 2032, says FMI​

Posted on August 11, 2022


The global AI platform market is expected to be worth [$10 billion (€9.67 billion)] in 2022, growing at a CAGR of 38.2% to be worth [$254.14 billion (€245.66 billion)] by the end of the forecast period of 2022-2032. The market was worth [$9.6 billion (€9.28 billion)] in 2021, and it is expected to grow by 4.2% year on year between 2021 and 2022, says Aditi Basu, marketing head at Future Market Insights.
Because decision-making is a critical point for service providers and various manufacturers in the market, the artificial intelligence (AI) platform market has grown. The AI platform market has boomed as players are now focusing on the creation of AI platforms and targeting niche solutions for solving specific enterprise problems, which has led to its growth.
The process that the AI platform goes through is learning which includes the acquisition of information for using the information, the other is reasoning which include using rules to reach approximate or definite conclusions, and the last is self-correction

Drivers and challenges​

One of the drivers for the AI platform market is the increase in the demand of AI-based solutions and products in the market. Another driver for the AI platform market include the surplus amount of data available and developed using the hardware or such other sources.
This helps the AI platform to work with full efficiency and reach out to the best possible results. Also, as there are technological advancements all round the world, there are growing innovation in the AI technology which would lead to the growth of AI platform market.
The ease of doing work is also a factor which is driving the market for AI platform as these platforms would help in the formation of intelligent business processes.
The factor which has been the major restraint for the AI Platform market is the skill gap which prevails in the market, and the use of AI platform mostly for the popular applications which keeps the other applications underdeveloped.

Market participants​

Examples of some of the market participants in the global AI platform market identified across the value chain include Microsoft Corporation, Google, Amazon Web Services, Infosys, Wipro, Premonition, Rainbird Technologies, Ayasdi, Inc., Mindmeld (Cisco Systems), Facebook, Vital AI, LLC, Kasisto, Receptiviti, Locl Interactive Inc., HPE, Qualcomm Technologies, and Absolutdata, Salesforce, IBM, Intel, and others of AI Platform market.

Regional overview​

On the basis of geography, the global AI platform market can be segmented into North America, Latin America, Europe, CIS & Russia, Asia Pacific excluding Japan, Japan, and the Middle East & Africa.
Among all these regions, North America is expected to hold a major market share of the global AI platform market during the forecast period, due to the early adoption of the AI platform based applications, and also due to the increased number of established players in the region regarding the AI platform market.

Market competition​

Some of the key participants present in the global AI Platform Cloud Service market include Oracle Corporation, Microsoft Corporation, IBM, Google LLC, Infosys Limited, Amazon Web Services, Wipro Limited, Baidu Inc., Cloudera Inc., Informatica LLC,among others.
Attributed to the presence of such high number of participants, the market is highly competitive. While global players such as Oracle Corporation, Microsoft Corporation, IBM, Google LLC., account for a considerable market size, several regional level players are also operating across key growth regions, particularly in the North America.

Recent developments​

Aditi Basu

• In August 2020, Google Cloud extended its partnership with Best Buy in a multi-year agreement for Best Buy’s Enterprise Data Platform. The partnership is expected to deliver enhanced technological experience, by using Artificial Intelligence (AI), analytics to create more personalised shopping experiences for customers of the latter.
• In April 2020, Siemens announced the availability of its MindSphere on Alibaba Cloud. The deployment and operation are expected to foster the industrial Internet of Things (IoT) in China.

These insights are based on a report on AI Platform cloud service market by Future Market Insights
The author is Aditi Basu, marketing head at Future Market Insights.
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[equanimous]

In regards to our discussion about GrAI Matter.

• former CEO Mr. Dinardo confirmed that AKD1000 being digital was directly scalable from 28nm to 14nm to 7nm and that at each scale down the performance would increase and the power consumption would go down at the same time
• TSMC 7nm to a 5nm for an NPU would reduce power consumption by 25% as stated below
• So what would the performance numbers be moving from 28nm to 5nm for Akida?.

Anil Mankar drove the decision to use 28nm because of three factors:

1. Yield - 28nm is a proven established process node and was most likely to be successfully produced first time round.

2. At 28nm the pricing is still relatively inexpensive but going smaller exponentially increases the price.

3. At 28nm the revolutionary best in class nature of AKD1000 are fully displayed and any sophisticated OEM or Tier 1 customer will understand that being digital performance and power use will only improve if they want to take it to a more expensive process mode such as 5nm as mentioned by Peter van der Made.

“Process Node as a Variant in TOPS/W
Understanding the underlying conditions for the testing helps establish an equal playing field for comparing TOPS results for different NPUs. Ideally, benchmarking conditions can be configured or interpolated to closely match your application requirement. But you will likely need to take steps when comparing NPUs including the normalization of processing frequency, the use of an identical neural network(s) with identical precision, and the application (or not) of sparsity and pruning throughout.

We now need to consider the process node as it relates to power consumption. One of the major advantages of moving to smaller process nodes is smaller nodes generally require less power for the same action. For instance, an NPU in TSMC 5nm will likely consume about 25% less power than the same NPU in TSMC 7nm. This is true for both IP and chip-based NPUs. Because process node has a powerful influence on performance it must be considered when comparing benchmark data. It is important to ask an NPU supplier whether the power numbers are measured in actual silicon, extrapolated from actual silicon, or are based on simulation. If actual silicon was used, then the vendor should indicate which process node”

Measuring NPU Performance

This blog post was originally published at Expedera's website. It is reprinted here with the permission of Expedera. There is a lot more to understanding

www.edge-ai-vision.com

 

[uiux]

In regards to our discussion about GrAI Matter.

• former CEO Mr. Dinardo confirmed that AKD1000 being digital was directly scalable from 28nm to 14nm to 7nm and that at each scale down the performance would increase and the power consumption would go down at the same time
• TSMC 7nm to a 5nm for an NPU would reduce power consumption by 25% as stated below
• So what would the performance numbers be moving from 28nm to 5nm for Akida?.

Anil Mankar drove the decision to use 28nm because of three factors:

1. Yield - 28nm is a proven established process node and was most likely to be successfully produced first time round.

2. At 28nm the pricing is still relatively inexpensive but going smaller exponentially increases the price.

3. At 28nm the revolutionary best in class nature of AKD1000 are fully displayed and any sophisticated OEM or Tier 1 customer will understand that being digital performance and power use will only improve if they want to take it to a more expensive process mode such as 5nm as mentioned by Peter van der Made.

“Process Node as a Variant in TOPS/W
Understanding the underlying conditions for the testing helps establish an equal playing field for comparing TOPS results for different NPUs. Ideally, benchmarking conditions can be configured or interpolated to closely match your application requirement. But you will likely need to take steps when comparing NPUs including the normalization of processing frequency, the use of an identical neural network(s) with identical precision, and the application (or not) of sparsity and pruning throughout.

We now need to consider the process node as it relates to power consumption. One of the major advantages of moving to smaller process nodes is smaller nodes generally require less power for the same action. For instance, an NPU in TSMC 5nm will likely consume about 25% less power than the same NPU in TSMC 7nm. This is true for both IP and chip-based NPUs. Because process node has a powerful influence on performance it must be considered when comparing benchmark data. It is important to ask an NPU supplier whether the power numbers are measured in actual silicon, extrapolated from actual silicon, or are based on simulation. If actual silicon was used, then the vendor should indicate which process node”

Measuring NPU Performance

This blog post was originally published at Expedera's website. It is reprinted here with the permission of Expedera. There is a lot more to understanding

www.edge-ai-vision.com

PVDM has stated 4nm is possible