Damo4
Regular
Most relevant BRN announcement today
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Most relevant BRN announcement today
robsmark
Regular
You see my point though…
Dijon101
Regular
Brainchip believer who questions whether GMAC intelligence partnership is worth anything. I think it's a legitimate question worth exploring instead of just blindly cheering.
Intel foundry
Mercedes
AMD
Edge impulse
Megachips
Renesas
Etc etc
We have a lot of great partners, is GMAC one of them ?? What do they bring to the table ??
Damo4
Regular
We'll see, 50% swings each month raises eyebrows.
I don't follow WBT, but let me know if they do end up with any speeding tickets.
I'd hate for everyone's optimism, for a better way to announce to ASX, being crushed when more companies get in trouble, as did Brainchip.
Fullmoonfever
Top 20
Watching for poss break....would need vol support if does, from TA view.
Prev Supp hit ~ 3 - 4 times so expect a bit of Resist on way back through.
Not on laptop so snip from mob.
Prev Supp hit ~ 3 - 4 times so expect a bit of Resist on way back through.
Not on laptop so snip from mob.
Damo4
Regular
Yeah there's quite a lot to like, but May/June was looking good, right up until it wasn't too.
I have a couple of features drawn a long time ago that are becoming important zones.
Also worth noting RSI and MACD set-ups.
One has to wonder how long people will sell off until only the diamond hands and instos own shares.
"Incorporating GMAC Intelligence into the Qualcomm Smart Cities Accelerator Program supports our growing ecosystem with real-time facial, vehicle and activity recognition for accurate and instantaneous decision making needed to scale IoT applications,” said Ashok Tipirneni, Director, Qualcomm Technologies, Inc. and Head of Platform Product Management for Smart Cities. “GMAC’s ‘GI4LL’ easy-to-deploy solutions for facial, license-plate and human-activity recognition are custom designed to support the development of smart cities, smart offices and smart homes, propelling Industry 4.0 forward for our ecosystem partners.”
Nothing.
HopalongPetrovski
I'm Spartacus!
I guess at a thousand dollars a month to deploy at an operation that runs 24/7 it equates to $1.38 an hour ( for a 30 day month) with no additional superannuation, sick leave, public holiday, payroll or leave expenses and removes the order taking and payment requirements from human staff.
So can lose those couple of human staff and their current wages along with all the additional potential problems that employing people brings.
We just now have to program the robots to start liking sausage McMuffins and cokes so we'll still have customers who can afford our wares.
wilzy123
Founding Member
SERA2g
Founding Member
Reckon the ice-cream machines will stop being down if AI takes over?I guess at a thousand dollars a month to deploy at an operation that runs 24/7 it equates to $1.38 an hour ( for a 30 day month) with no additional superannuation, sick leave, public holiday, payroll or leave expenses and removes the order taking and payment requirements from human staff.
So can lose those couple of human staff and their current wages along with all the additional potential problems that employing people brings.
We just now have to program the robots to start liking sausage McMuffins and cokes so we'll still have customers who can afford our wares.
Tothemoon24
Top 20
I haven’t had a look at Megachips webpage for a while , looks bloody fantastic to me
Solution (Application of Image Processing)
Customers can confirm actual operation, functions and performance with their own system.
This kit enables a performance evaluation by simulation in conjunction with hardware function and performance evaluation.
Using IP of our Edge AI partners brings you further added values.
We co-design subsystem incorporating characteristic Edge AI IP according to the customer’s system. (We can also support specified AI IP.)
Solution (Application of Image Processing)
Edge AI
What is Edge AI?
Edge AI performs computation that is necessary for AI at the edge device of each field without sending the data to the cloud. The stand-alone operation without connecting to the internet can protect privacy, achieve real-time performance, ensure security, so it is advantageous to the cloud.
MegaChips Edge AI Solution
We have 2 kinds of solutions of AI cores to realize Edge AI.- BrainChip: Lower cost / Low power consumption AI accelerator (Support on-chip learning）
- Quadric: General-purpose AI processor (DSP/CPU functions incorporated)
BrainChip
Combining BrainChip’s revolutionary neuromorphic technology that holds on-chip training function with MegaChips advanced integration technology will contribute to solve customer’s various problems.
Quadric
Combining Quadric’s industry’s first general-purpose neural processing unit (GPNPU) with MegaChips ASIC solutions provides customers a wide range of solutions in the products with Edge AI functions.
Evaluation Kit
Customers can confirm actual operation, functions and performance with their own system.
This kit enables a performance evaluation by simulation in conjunction with hardware function and performance evaluation.
Major IP/Subsystem for Image Processing
MegaChips maintains a development platform of various IP and subsystem that are necessary for the image processing and provide optimal ASIC to meet customer needs in a short period of time.Using IP of our Edge AI partners brings you further added values.
We co-design subsystem incorporating characteristic Edge AI IP according to the customer’s system. (We can also support specified AI IP.)
- Example 1: Intuitive UI subsystem (BrainChip IP)
- Example 2: Image AI subsystem (Quadric IP)
With regards Esq 111...I think my figure of 20 is correct, going by what the company has officially confirmed by the home website over the last 5 months or so, maybe I'm totally wrong, but I'm normally not that far off the mark, we probably should get Tony to give us all an accurate figure, that is, who's been recognized as an "official" partner so to speak, you are probably correct, but I'm not 100% convinced your list is current.
All's cool...by the way, our Founder is still in the US working hard on Akida II ..you have to love Peters passion, he's never afraid of hard work,
I think the more you get to know him, Brainchip isn't about the money, it's about passion, drive, trying to make a difference in this world for the betterment of mankind, having a gift and sharing it is the most unselfish thing that you can donate, and that's yourself, soul, spirit. the lot.
We are blessed to have such a solid team, guiding us and leading us...take a moment to be grateful....sermon over
Tech...by the way you should see the night sky (milky way) up at the top of NZ, no city lights, it's very remote, the stars are so, so bright !
Schnitzel lover
Regular
Can we assume that they are all either EAPs or Megachips customers.. Otherwise how are they all using Akida without an IP license?
Getupthere
Regular
I think they are all part of the over 100 NDA’s Lou mentioned 3 years ago.
Looks like the NDA’s are slowly being relaxed.
Looks like the NDA’s are slowly being relaxed.
Schnitzel lover
Regular
Needs more time still. Any up moves will hit supply for a while until it builds a decent base. BRN bases with so many shares on issue (SOI) tend to be lengthier than many other tech stocks that have much lower SOI’s.
And with no new meaningful revenue or licensees evident, there’s no buying power to churn through that supply..
Schnitzel lover
Regular
If they’re NDAs, then the mark of Gen 2 worth is in new licensee signing over the next 6-12months, and probably the sanity of many faithful long term share holders
opinion only dyor
wilzy123
Founding Member
Fullmoonfever
Top 20
Not a bad article to read through with the pulling together of some industry info and views from other analyst sources as well.
Both the PC and automotive AI chip markets are growing faster than the data center AI chip market at over 30% each, at this pace they will surpass data center’s market size by 2025.
Edge inferencing is likely to be dominated by existing vendors, all of which are investing heavily in supporting transformers and LLMs. So, what opportunities exist for new entrants? Automotive partnerships could be the hope; second, supplying IP or chiplets to one of the SoC vendors; and, creating customized chips for intelligent edge devices that can afford the cost.
Nvidia is clearly the leader in the market for training chips, but that only makes up about 10% to 20% of the demand for AI chips. Inference chips interpret trained models and respond to user queries. This segment is much bigger, and quite fragmented, not even Nvidia has a lock on this market. Techspot estimates that the market for AI silicon will comprise about 15% for training, 45% for data center inference, and 40% for edge inference. The serviceable market for foundation model training will likely remain too small to support large companies, thereby relatively low acquisition offers are possible. Where is the opportunity?
Data center, automotive, and PC, these three sectors take 90% of the AI chip market if excluding the market of smartphones and smartwatches (to prevent data bias from Apple and Samsung), but the data center has 6 vendors taking 99% of market share, that market is saturated.
Both the PC and automotive AI semiconductor markets are growing faster than the data center AI semiconductor market at over 30% each, at this pace they will surpass data center’s market size by 2025.
Custom chips and startups can outperform the chip giant on specific inference tasks that will become crucial as large language models are rolled out from cloud data centers to customer environments – Inferencing at the Edge. The term ‘edge’ is referring to any device in the hands of an end-user (phones, PCs, cameras, robots, industrial systems, and cars). These chips are likely to be bundled into a System on a Chip (SoC) that executes all the functions of those devices.
References:
Inferring the future of AI chips, Pitchbook
邊緣 AI（Edge AI）的半導體創新機會
Here is an introduction to the AI chip industry landscape and highlights with selected infographics and resources.
Full AI infrastructure stack from Intel Capital
Intel’s Naveen Rao says that the compute capacity needed to handle increasing model will need to be improved by 10x every year, and to achieve the required 10X improvement every year it will take 2X advances in architecture, silicon, interconnect, software and packaging.
There are many innovations to improve performance, here Moor Insights & Strategy created a chart to highlight them (beyond the use of lower precision, tensor cores and arrays of MACs), but AI hardware is harder than it looks.
IBM also has a technology roadmap for the future of AI chips.
But, speed/performance isn’t the only key metric, it’s always about trade-offs between power, performance, and area (PPA) and optimize them for specific applications.
The trade-off between Performance (in GOP/sec) versus Power(image from IMEC and Beil8).
Feature requirements are different in different use cases and hence different choices of AI chip architectures. McKinsey makes some examples here.
McKinsey also made the conclusion that AI ASIC chips will have the biggest growth among all.
On the AI chip landscape, there are 80 startups globally attracted $10+ billion funding, and 34 established players. Most startups focus on inferencing, avoid competing with Nvidia. Kisaco Research created a conceptual triangle to represent the three major segments of AI accelerators.
The building or joining the industry ecosystems is life-or-death for AI chip companies, it’s the final products that matter, not only the AI chip metrics. The overall performance of final AI applications needs other components (like data) to be built and play out well with the AI chips. Big gains in performance are only going to come through an entire systems approach. Data movement on and off chip is a bottleneck, computation is just a component of a total systems approach to the solution.
“Investors today are looking at the semiconductor world…I think AI is pretty clearly going to be the next things that’s going to really scale up and drive the semi-conductor industry, so nothing attracts opportunity like AI at the moment.” — Brett Simpson, Partner & Co-Founder, Arete Research (from AI Hardware Summit hosted by Kisaco Research)
“Developing deep learning models is a bit like being a software developer 40 years ago. You have to worry about the hardware and the hardware is changing quite quickly… Being at the forefront of deep learning also involves being at the forefront of what hardware can do.” — Phil Blunsom, Department of Computer Science at Oxford University and DeepMind (from AI Hardware Summit hosted by Kisaco Research)
AI Inference Chips Get the Most Attention from Semiconductor VC
by WiseOcean | Jun 18, 2023 | AI Chip, Major TrendsKey takeaways
VC investors have already shifted their focus to categories in which startups can carve out AI chip market share. In 2021 and 2022, AI/ML VC deal value in inferencing has become significantly larger than training-focused only, breaking a historical trend.Both the PC and automotive AI chip markets are growing faster than the data center AI chip market at over 30% each, at this pace they will surpass data center’s market size by 2025.
Edge inferencing is likely to be dominated by existing vendors, all of which are investing heavily in supporting transformers and LLMs. So, what opportunities exist for new entrants? Automotive partnerships could be the hope; second, supplying IP or chiplets to one of the SoC vendors; and, creating customized chips for intelligent edge devices that can afford the cost.
PC and automotive AI chip markets
AI computing remains a major growth driver for the semiconductor industry, and at $43.6 billion in 2022, the market remains large enough to support large private companies. Also, the AI semiconductor market is divided into companies in China and those outside of China, because of the current political circumstance.Nvidia is clearly the leader in the market for training chips, but that only makes up about 10% to 20% of the demand for AI chips. Inference chips interpret trained models and respond to user queries. This segment is much bigger, and quite fragmented, not even Nvidia has a lock on this market. Techspot estimates that the market for AI silicon will comprise about 15% for training, 45% for data center inference, and 40% for edge inference. The serviceable market for foundation model training will likely remain too small to support large companies, thereby relatively low acquisition offers are possible. Where is the opportunity?
Data center, automotive, and PC, these three sectors take 90% of the AI chip market if excluding the market of smartphones and smartwatches (to prevent data bias from Apple and Samsung), but the data center has 6 vendors taking 99% of market share, that market is saturated.
Both the PC and automotive AI semiconductor markets are growing faster than the data center AI semiconductor market at over 30% each, at this pace they will surpass data center’s market size by 2025.
Inferencing at the Edge
In the past 2 years, we saw a 69.0% decline in year-over-year VC funding for AI chip startups outside of China, VC investors have already shifted their focus to categories in which startups can carve out market share. In 2021 and 2022, AI/ML VC deal value in inferencing has become significantly larger than training-focused only, breaking a historical trend. Also, edge computing demands are driving more commercial partnerships for inference-focused chips than for cloud training chips.Custom chips and startups can outperform the chip giant on specific inference tasks that will become crucial as large language models are rolled out from cloud data centers to customer environments – Inferencing at the Edge. The term ‘edge’ is referring to any device in the hands of an end-user (phones, PCs, cameras, robots, industrial systems, and cars). These chips are likely to be bundled into a System on a Chip (SoC) that executes all the functions of those devices.
What opportunities exist for new entrants?
Edge inferencing is likely to be dominated by existing vendors of traditional silicon, all of which are investing heavily in supporting transformers and LLMs. So, what opportunities exist for new entrants?- Supply IP or chiplets to one of the SoC vendors. This approach has the advantage of relatively low capital requirements; let your customer handle payments to TSMC. There is a plethora of customers aiming to build SoCs.
- Find some new edge devices that could benefit from a tailored solution. Shift focus from phones and laptops to cameras, robots, drones, industrial systems, etc. But some of these devices are extremely cheap and thus cannot accommodate chips with high ASPs. A few years ago, many pitches for companies looking to do low-power AI on cameras and drones. Very few have survived. But, edge computing has become more prevalent in the trend of “smart everything”, and computing platforms also extend into wearables such as Mixed Reality headsets, technology advancements always push new possibilities.
- Automotive partnerships could be the hope, this market is still highly fragmented, but the opportunity is substantial. In Q2 2022, edge AI chip startup Hailo announced a partnership with leading automotive chipmaker Renesas for self-driving applications.
References:
Inferring the future of AI chips, Pitchbook
邊緣 AI（Edge AI）的半導體創新機會
AI Hardware Landscape and Highlights
by WiseOcean | Sep 29, 2020 | AI ChipHere is an introduction to the AI chip industry landscape and highlights with selected infographics and resources.
Full AI infrastructure stack from Intel Capital
Intel’s Naveen Rao says that the compute capacity needed to handle increasing model will need to be improved by 10x every year, and to achieve the required 10X improvement every year it will take 2X advances in architecture, silicon, interconnect, software and packaging.
There are many innovations to improve performance, here Moor Insights & Strategy created a chart to highlight them (beyond the use of lower precision, tensor cores and arrays of MACs), but AI hardware is harder than it looks.
IBM also has a technology roadmap for the future of AI chips.
But, speed/performance isn’t the only key metric, it’s always about trade-offs between power, performance, and area (PPA) and optimize them for specific applications.
The trade-off between Performance (in GOP/sec) versus Power(image from IMEC and Beil8).
Feature requirements are different in different use cases and hence different choices of AI chip architectures. McKinsey makes some examples here.
McKinsey also made the conclusion that AI ASIC chips will have the biggest growth among all.
On the AI chip landscape, there are 80 startups globally attracted $10+ billion funding, and 34 established players. Most startups focus on inferencing, avoid competing with Nvidia. Kisaco Research created a conceptual triangle to represent the three major segments of AI accelerators.
IMEC identified the rise of edge AI chip industry as one of five trends that will shape the future of the semiconductor technology landscape.
Edge computing is an emerging industry with very active activities (new companies, fundraising, and M&A) in 2020. MobiledgeX and Topio Networks, partnering with Seamster, embarked upon an exhaustive, multi-month research initiative to understand what market segments will benefit the most from edge computing. To identify the product-market-technology fit, check out the Edge Market Navigator.With an expected growth of above 100% in the next five years, edge AI is one of the biggest trends in the chip industry. As opposed to cloud-based AI, inference functions are embedded locally on the Internet of Things (IoT) endpoints that reside at the edge of the network, such as cell phones and smart speakers. The IoT devices communicate wirelessly with an edge server that is located relatively close. This server decides what data will be sent to the cloud server (typically, data needed for less time-sensitive tasks, such as re-training) and what data gets processed on the edge server.
Compared to cloud-based AI, in which data needs to move back and forth from the endpoints to the cloud server, edge AI addresses privacy concerns more easily. It also offers advantages of response speeds and reduced cloud server workloads. Just imagine an autonomous car that needs to make decisions based on AI. As decisions need to be made very quickly, the system cannot wait for data to travel to the server and back. Due to the power constraints typically imposed by battery-powered IoT devices, the inference engines in these IoT devices also need to be very energy efficient.
The building or joining the industry ecosystems is life-or-death for AI chip companies, it’s the final products that matter, not only the AI chip metrics. The overall performance of final AI applications needs other components (like data) to be built and play out well with the AI chips. Big gains in performance are only going to come through an entire systems approach. Data movement on and off chip is a bottleneck, computation is just a component of a total systems approach to the solution.
“Investors today are looking at the semiconductor world…I think AI is pretty clearly going to be the next things that’s going to really scale up and drive the semi-conductor industry, so nothing attracts opportunity like AI at the moment.” — Brett Simpson, Partner & Co-Founder, Arete Research (from AI Hardware Summit hosted by Kisaco Research)
“Developing deep learning models is a bit like being a software developer 40 years ago. You have to worry about the hardware and the hardware is changing quite quickly… Being at the forefront of deep learning also involves being at the forefront of what hardware can do.” — Phil Blunsom, Department of Computer Science at Oxford University and DeepMind (from AI Hardware Summit hosted by Kisaco Research)
Cartagena
Regular
Hi all.
New contributor here.
Did some more research and found this. It's interesting that they have tech that is very similar to Brainchip. Low energy and not reliant on cloud. See below extract :
The single board computers can also be used in social infrastructure. SPRESENSE installed on utility poles can monitor sounds and detect noises, screams, and other abnormalities. Attempting to do this through the cloud would jam telecommunications lines, but SPRESENSE and LPWA (communication method) used together can engage AI functions with low power consumption. The word "sensing" immediately brings to mind images, but in actuality, sounds are also extremely useful forms of sensing. Sensing and AI are definitely approaching the level of the five senses of humans.
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