buena suerte :-)
BOB Bank of Brainchip
Wow .... certainly looks like it!! Great find AARON
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
wilzy123
Founding Member
The AU version of the US one already granted: https://company-announcements.afr.com/asx/brn/33114b39-49b4-11ed-810a-2616002949eb.pdf
Wags
Regular
Great find. This may explain in part, the issue of employee incentive shares.
thelittleshort
Top Bloke
New position advertised - Technical Sales Engineer
Job Title: Technical Sales Engineer - 2-4 years experience - ONSITE
Reports to: VP of Sales
**No Agencies Please**
SUMMARY:
The Technical Sales Engineer is an exempt, full time, individual contributor role that will support both pre and post sales design and implementation activities for complex technical products. This position will review customer technical specifications and recommend specific products or services. In addition, the technical sales engineer will review sales proposals for accuracy and deliver technical product presentations, trainings, and materials used to engage customers and educate the sales team. This position is an exempt, individual contributor role that will report to the VP of Sales.
ESSENTIAL JOB DUTIES AND RESPONSIBILITIES:
- Supports both pre-sales and post-sales design and implementation activities.
- Reviews customer technical specifications, recommends specific products or services, estimates costs and efforts to implement.
- Plans and designs the configuration of products for initial implementation or the deployment of custom solutions, enhancements, and upgrades.
- Troubleshoots problems and oversees the completion of repairs, workarounds, or customizations.
- Delivers technical product trainings to customers and internal audiences.
- Internally focused liaison between the field, product, and engineering team
- Detailed responses to the field team related to engineering related customer questions.
- Assist in building and maintenance of the FAQ database.
- Lead engagement with engineering including bug reporting/resolution process.
- Continuous competitive analysis and benchmarking
- Own the technology demonstrations (plan, execute, and fully document)
- Lead the definition, design reviews, and launch of optimized evaluation systems including full documentation package.
- Application-level s/w development and debugging.
- Collaborate with software and hardware development teams to troubleshoot problems and develop solutions.
I love how a small ad for sales person gets people very excited about the stock. Lol
Wish I can do the same for my Woolworths holdings.
chapman89
Founding Member
It shows growthI love how a small ad for sales person gets people very excited about the stock. Lol
Wish I can do the same for my Woolworths holdings.
It shows confidence
It shows things are happening in many different parts of the world,
And it shows shareholders that despite the share price, they’re getting on with the job and moving forward with the plan they have.
There’s never been a more exciting time to be a Brainchip shareholder with all that’s happening.
It would seem Renesas will be first to tape out and have commercial products containing akida IP, I see this as a huge huge re rate of our stock, because a lot of the outside comments we get is
“unproven technology, no real world applications despite already being showcased by Socionext VVDN NVISO Renesas Mercedes, no revenue”
It’ll shut all those who say such things and rightly so, potential investors and investors can see that revenue will be coming in.
No point watching the kettle boil, just let it boil until you hear it boom
It shows growth
It shows confidence
It shows things are happening in many different parts of the world,
And it shows shareholders that despite the share price, they’re getting on with the job and moving forward with the plan they have.
There’s never been a more exciting time to be a Brainchip shareholder with all that’s happening.
It would seem Renesas will be first to tape out and have commercial products containing akida IP, I see this as a huge huge re rate of our stock, because a lot of the outside comments we get is
“unproven technology, no real world applications despite already being showcased by Socionext VVDN NVISO Renesas Mercedes, no revenue”
It’ll shut all those who say such things and rightly so, potential investors and investors can see that revenue will be coming in.
No point watching the kettle boil, just let it boil until you hear it boom
Excellent Post J.......fact after fact.....great to see our two Perth Data Scientist/s Engineers named as inventors in the latest AU Patent, that is,
Keith and Milind...top stuff, our Perth office is full of brilliant talent!
Tech
Hi Aaronasx,
This is a great find.
Interesting that neither PvdM nor Anil is mentioned as an inventor, but Milind Joshi is. Milind is our patent attorney in Perth.
The priority date is December 2021, so the 18 month NDA ends in May 2023.
Maybe this is our LSTM/Transformer patent application?
58 minutes ago - 2 applicants. The tech layoffs will ensure there is no shortage of quality candidates.
thelittleshort
Top Bloke
Excellent Post J.......fact after fact.....great to see our two Perth Data Scientist/s Engineers named as inventors in the latest AU Patent, that is,
Keith and Milind...top stuff, our Perth office is full of brilliant talent!
Tech
Hey @TECH you know who Douglas McLelland is? Seems to be the only one on the list external to BrainChip
Bravo
If ARM was an arm, BRN would be its biceps💪!
Here's what Adam White, President of Infineon Technologies’ Power and Sensor Systems division said in an interview in November 2022.
EXTRACT ONLY
EXTRACT ONLY
AARONASX
Holding onto what I've got
Douglas seems to know his stuff!
Douglas McLelland
BrainChip - Cité(e) 398 fois - Computational Neuroscience - Spiking Neural Networks - Neural Network Quantization
5 reasons MLops teams are using more Edge ML
5 reasons MLops teams are using more edge ML — VentureBeat
As the number of machine learning (ML) use cases grows and evolves, an increasing number of MLops organizations are using more ML at the edge — that is, they are investing in running ML models on devices at the periphery of a network, including smart cameras, IoT computing devices, mobile...apple.news
As the number of machine learning (ML) use cases grows and evolves, an increasing number of MLops organizations are using more ML at the edge — that is, they are investing in running ML models on devices at the periphery of a network, including smart cameras, IoT computing devices, mobile devices or embedded systems.
ABI Research, a global technology intelligence firm, recently forecast that the edge ML enablement market will exceed $5 billion by 2027. While the market is still in a “nascent stage,” according to Lian Jye Su, research director at ABI Research, companies looking to ease the challenges of edge ML applications are turning to a variety of platforms, tools and solutions to boost an end-to-end MLops workflow.
“We are absolutely seeing MLops organizations increase the use of EdgeML,” said Lou Flynn, senior product manager for AI and analytics at SAS. “Enterprises big and small are running to the cloud for various reasons, but the cloud doesn’t lend itself to every use case. So organizations from nearly every industry, including aerospace, manufacturing, energy and automotive, leverage Edge AI to gain competitive advantage.”
Here are five reasons MLops teams are giving edge ML a thumbs-up:
1. Edge devices have become faster and more powerful.
“We have seen multiple companies focus on end-to-end processes around edge ML,” said Frederik Hvilshøj, lead ML engineer at data-centric computer vision company Encord. The two major reasons, he explained, are: Edge devices have become increasingly powerful while model compression has become more effective, which allows for running more powerful models at a higher speed; and edge devices also typically live much closer to the data source, which removes the necessity to move big volumes of data.
“The combination of the two means that high performance models can be run on edge devices at a close-to-real time speed,” he said. “Previously, GPUs living on central servers were necessary to get the high model throughput — but at the cost of having to transfer data back and forth, which made the use case less practical.”
2. Edge ML offers greater efficiency.
Today’s distributed data landscape is ripe with opportunity to analyze content to gain efficiencies, said Lou Flynn, senior product manager for AI and analytics at SAS.
“Many data sources originate from remote locations, such as a warehouse, a standalone sensor at a large agricultural site or even a CubeSat [a square-shaped miniature satellite] as part of a constellation of electro-optical imaging sensors,” he explained. “Each of these scenarios depicts use cases that could gain efficiencies by running edge ML vs. waiting for data to reconcile in cloud storage.”
3. Bandwidth and cost savings are key.
“You need to run ML models on the edge because of physics (bandwidth limitations, latency) and cost,” said Kjell Carlsson, head of data science strategy at Domino Data Lab. Carlsson explained that IoT is not feasible if data from every sensor needs to be streamed to the cloud to be analyzed.
“The network in a supermarket would not support the high-definition streaming from a couple dozen cameras, let alone the hundreds of cameras and other sensors you would want in a smart store,” he said. By running ML on the edge, you also avoid the cost of data transfer, he added.
“For example, a Fortune 500 manufacturer is using edge ML to continuously monitor equipment to predict equipment failure and alert staff to potential issues,” he said. “Using Domino’s MLops platform, they are monitoring 5,000+ signals with 150+ deep learning models.”
4. EdgeML helps scale the right data.
The real value of edge ML, said Hvilshøj, is that with distributed devices, you can scale your model inference without having to buy larger servers.
“With scaling inference out of the way, the next issue is collecting the right data for the next training iteration,” he said. In many cases, collecting raw data is not hard, but choosing data to label next becomes hard for large volumes of data. The compute resources on the edge devices can help identify what might be more relevant to label.
“For example, if the edge device is a phone and the user of the phone dismisses a prediction, this can be a good indicator that the model was wrong,” he said. “In turn, the particular piece of data would be good for retraining the model with proper labels.”
5. MLops organizations want more flexibility.
According to Flynn, MLops organizations should use their models to not only make better decisions, but to optimize these models for different hardware profiles — for example, using technology like the Apache TVM (Tensor Virtual Machine) to compile models to run more efficiently on different cloud providers and across devices with varying hardware (CPU, GPU and/or FPGAs). One SAS customer — Georgia-Pacific, an American pulp and paper company — uses edge computing at many of its remote manufacturing facilities where high-speed connectivity often isn’t reliable or cost-effective.
“This flexibility gives MLops teams agility to support a wide variety of use cases, enabling them to bring processing to their data on a growing pool of devices,” Flynn said. “While the range of devices are vast, they often come with resource limitations that could constrain model deployment. This is where model compression comes into play. Model compression reduces the footprint of the model and enables it to run on more compact devices (like an edge device) while improving the model’s computational performance.”
I hadn't thought the supermarket case was so strong. You have mains power and a second or two of latency is not critical, but when you add several 'cash" registers scanning items with CNN that creates an overload for the shop's comms network. Everything here is Akida to a tee - compressed model library - ML - speed - reduced data transfer ...
Hi. Doug has been with us for a number of years now, based at of LA or attached to our US office..he's been named a few times over the years as an engineer working on different projects for the company.
Regards...Chris
thelittleshort
Top Bloke
Taproot
Regular
I know this post by Luca above is old news, but closer inspection reveals more.
Adam Osseiran's comment on Luca Verre's post, well and truly puts to bed any speculation regarding the time that Prophesee and Brainchip have been aware of each others existence.
The reactions list is something well worth delving into. A total of 269 likes / loves etc. is a very small percentage of the world's total population. You can then weed out a few of the TSE members that liked his post, you can also weed out those connected to Brainchip and Prophesee. You can weed out Insto's, tech journo's / research etc. What remains is quite amazing.
Likes that are relevant to the motion blur speculation include:
Michel Sarkis
Principal Engineer / Mgr at Qualcomm
Evgeni Gousev
Senior Director at Qualcomm
Michael Scherer
Advanced Camera Technology at Samsung
Another group of individuals that stood out from this post by Luca were from Meta.
Hui Chen
Optical Scientist at Meta
Chris Harrison
AI Recruiter at Meta
Pietro Caragiulo
AR / VR Silicon at Meta
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