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DollazAndSense
Regular
I googled Snicko and I still don't get it.
Evermont
Stealth Mode
BLOGSEPTEMBER 20, 2023
The Future of Vision: Edge AI Breaks Barriers for Data-Intensive Applications
Transforming industries and enhancing efficiency with cutting-edge AI vision technology.
By Parag Beeraka, Senior Director, Segment Marketing, IoT, Arm
One of the most promising applications of IoT AI vision technology is to capture consumer data inside stores so retailers can more quickly and efficiently optimize product placement, store layout and customer experience based on video data.
But there are two major hurdles to overcome: Cost and complexity. A large grocery store that wants to harvest foot-traffic, purchase and other data would need about 15,000 cameras in store. At 30 frames per second of 4K video, those 15,000 cameras would produce 225 gigabits of data per second.
That happens because video data is enormous, compared with other forms of data, and intricate processing is required, including image recognition, object detection, and scene analysis. These AI vision tasks often require advanced algorithms and models, contributing to the computational complexity. On top of that, big data like that needs to be sent to the cloud for efficient computation and then back out for decision-making.
Clearly, 225 gigabits per second is uneconomical.
But that’s only if you think it’s still 2018, not 2023. Much has changed in the past five years. The combination of improved and more-efficient processing at the edge, coupled with AI and machine learning, now chips away at that big uneconomic roadblock in front of many promising vision applications.
Unlocking edge AI vision innovation
Back then, too many vital technologies sat siloed, each either difficult or impossible to integrate with other important puzzle pieces to enable a frictionless innovation ecosystem. In a homogenous processing world it was difficult to be able to customize solutions for different vision workloads when one size had to fit all. What’s different about today?
Engineers and developers have attacked cost and complexity, as well as other challenges. Take the processing complexity challenge for example. One pathway to driving down the cost and complexity of vision solutions is to offer developers more flexibility in how they implement edge solutions – heterogeneous compute.
Designers are producing increasingly powerful processors that offer higher computational capacity while remaining energy efficient. These processors include CPUs, GPUs, ISPs and accelerators designed to handle complex tasks like AI and machine learning in sometimes resource-constrained environments. In addition, AI accelerators – either as a core on an SoC or as a stand-alone SoC – enable efficient execution of AI algorithms at the edge.
Tackling complexity
Let’s take one piece of the complexity puzzle. Arm in 2022 introduced the Arm Mali-C55, its smallest and most high-performance Image Signal Processor (ISP) to date. It features a blend of image quality, throughput, power efficiency, and silicon area, for applications like endpoint AI, smart home cameras, AR/VR, and smart displays. It achieves higher performance with throughput of up to 1.2Gpix/sec, making it a compelling choice for demanding visual processing tasks. And, when it comes to the push toward heterogeneous compute, the Mali-C55 is designed for seamless integration in SoC designs with Cortex-A or Cortex-M CPUs.
That’s key because in SoCs ISP output is often linked directly to a machine learning accelerator for further processing using neural networks or similar algorithms. This involves providing scaled-down images to the machine learning models for tasks like object detection and pose estimation.
This synergy, in turn, has given rise to ML-enabled cameras and the concept of “Software Defined Cameras.” And this allows OEMs and service providers to deploy cameras globally with evolving capabilities and revenue models tied to dynamic feature enhancements.
Think for example, of a car parking garage with cameras dangling above every slot, determining whether the slot is filled or not. That was great in 2018 for drivers entering the garage needing to know where the open slots are at a glance, but not an economical solution in 2023. How about leveraging the notion of edge AI and positioning only one or two cameras at the entrance and exit or on each floor and having AI algorithms figure out the rest? That’s 2023 thinking.
That brings us back to the retailer’s problem: 15,000 cameras producing 225 gigabits per second of data. You get the picture, right?
Amazon has recognized this problem and in the latest version of its Just Walk Out store technology, it increased the compute capability in the camera module itself. That’s moved the power of AI to the edge, where it can be more efficiently and quickly computed.
With this powerful, cost-effective vision opportunity, a grocery retailer might, for example, analyze video data from in-store cameras and determine that it needs to restock oranges around noon every day because most people buy them between 9-11 a.m. Upon further analysis, the retailer realizes that a lot of your customers – anonymized in the video data for privacy reasons – also buy peanuts during the same shopping trip. You use this video data to change your product placement.
Right place, right compute
This kind of compute optimization – putting the right type of edge AI computing much closer to the sensors – reduces latency, can improve tighten security and reduce costs. It also can spark new business models.
One such business model is video surveillance-as-a-service (VSaaS). VSaaS is the provision of video recording, storage, remote management and cybersecurity in the mix of on-premises cameras and cloud-based video-management systems. The VSaaS market is expected to reach $132 billion by 2027, according to Transparency Market Research.
At a broader level, however, immense opportunity awaits because so many powerful potential applications have been waiting in the wings because of economics, processing limitations or sheer complexity.
Consider:
Leading-edge adopters aren’t waiting. In South Korea’s Pyeongtaek City, city leaders are planning to build a test bed using smart city technologies such as artificial intelligence and autonomous driving to be completed in 2025 and spread throughout the city.
- Smart Cities: Video analytics for traffic management, pedestrian flow analysis, and parking space optimization in smart cities can lead to substantial data generation.
- Industrial Automation: Quality control, defect detection, and process optimization.
- Autonomous Vehicles: The sensors and cameras on autonomous vehicles, such as self-driving cars and drones capturing data for navigation and safety systems, perceiving their surroundings in real time.
- Virtual Reality (VR) and Augmented Reality (AR): Immersive VR and AR experiences require rendering and processing of high-resolution visual content in real time, resulting in significant data generation.
The city of a half-million people grapples with traffic congestion and pedestrian fatalities. As part of a citywide “smart city” overhaul, experts have deployed Arm partner Nota.ai’s Nespresso platform – an automatic AI model compression solution – in vision devices to create an intelligent transportation system.
At the device level, clever design is helping customers achieve their vision visions. Take the Himax Wiseye-II, a smart image sensing solution that can be deployed in a range of battery-operated consumer and home security applications, including notebooks, doorbell, door lock, surveillance camera and smart office. It marries Arm microcontroller and neural processor cores to drive machine vision AI more deeply into consumer and smart-home devices.
These examples and future innovation being designed today are happening because of amazing advances in edge AI technology. And in vision, they’re being built on Arm.
In addition to hardware, Arm makes the journey for developers of image solutions faster and more efficient, thanks for software libraries, interconnect standards, security frameworks, and development tools such as Arm Virtual Hardware, which allows them to run applications in a virtual on their target architecture before committing to hardware.
So remember those hopes of transforming the world with previously untapped amounts of data using vision technology that once seemed a far-off dream because of cost and complexity? They can become reality now.
What Are the Key Strategies for AI Vision to Tackle High Costs and Complex Data Processing?
The integration of vision, AI and edge computing is revolutionizing data-intensive applications, overcoming cost and complexity challenges. What's the potential of AI-driven vision solutions in everything from retail to smart cities?newsroom.arm.com
Interesting. Click the link and it takes you to the Wevolver Edge AI Report.
Evermont
Stealth Mode
Sorry @Tothemoon24 missed your earlier post!
David holland
Regular
Why do people dream, how long was it the Megachip deal was announced
Does this indicate the former CEO has sold his shares ? Or just no longer holds enough to make the list ?
DingoBorat
Slim
"When we changed to being IP only provider, my potential customer lost interest due to the cost and time involved in creating a custom chip to suit the application"
Wouldn't AKIDA 1500 now satisfy that requirement?
The Company has now expanded its market introduction strategy and may be more open to such an opportunity.
I think we would have enough irons in the fire as it is, they are just not hot enough yet..
No stone, should be left unturned though, if it can help accelerate market adoption.
DingoBorat
Slim
"I make more than that in 3 months. Hell my bonus was bigger than that"
So you're making well over 2 grand a week and your (I'm assuming yearly) bonus was over 27k and you're having a sook about your investment in BrainChip?..
Gee, I wish I could be in such a dire financial predicament..
Just try and batten down the hatches and huddle with your family over a hot peppermint a little longer..
I'm sure you'll make it Champ
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jtardif999
Regular
LDN no more
BigDonger101
Founding Member
buena suerte :-)
BOB Bank of Brainchip
FabricatedLunacy
Regular
Completely missing the point DB. I never said I was in a dire financial predicament.."I make more than that in 3 months. Hell my bonus was bigger than that"
So you're making well over 2 grand a week and your (I'm assuming yearly) bonus was over 27k and you're having a sook about your investment in BrainChip?..
Gee, I wish I could be in such a dire financial predicament..
Just try and batten down the hatches and huddle with your family over a hot peppermint a little longer..
I'm sure you'll make it Champ
I am not annoyed that I am losing money, I am annoyed at the complete lack of traction and/or any visibility of our CEO... Yes we also say there is lots and yes things take time etc. But our CEO is ont our there reassuring anyone, we just dive in SP and everything is hidden behind NDA as we watch the financials...
And, yes I understand that not everyone is as fortunate in terms of their pay, and trust I work for it, was lucky to be in the right place and it absolutely did not come overnight.
jtardif999
Regular
Our move to being an IP company over being just a chip company will imo eventually make us very very rich. There’s around 97% clear profit in it, but to get that we need to be more patient with the scheme of things on the IP path. Through IP BrainChips tech will scale the length and breadth of the market and when it starts the royalties will very quickly snowball imo. If we were just producing the chips the overheads would be much greater and the profit a lot smaller. Perhaps there’s opportunity for both IP and chips particularly in the space sphere but I don’t think it will ever be extensive.I'm not usually a conspiracy theorist but the lack of results and the lack of any meaningful explanation over the last 12 months has me wondering about BRN. Are our people not capable of selling this technology? Are we being set up for a takeover? Was our move to an IP only company a strategic mistake? The silence is deafening.........
Most of we long term investors put our faith in PVdM and Anil when we bought in. I for one would like to hear from them directly about commercial progress without everyone ducking for cover behind the NDA's.
toasty
Regular
Unfortuntely, like many such scenarios, the opportunity was lost at the time BRN decided not to engage. The client found an alternative system which, although not as efficient and elegant as Akida, could get the job done..........in a fashion. I suspect the availability of other "good enough" alternatives is one of the reasons for the delay in uptake of Akida.
DingoBorat
Slim
There is a heap of traction happening, it's just not showing on the books yet.
I think anyone that can't see that, has got to be blind or something man..
And I do understand the frustration of financial advancement, not happening as quickly as we would like.
But it is what it is.
I'm sure Sean and all the BrainChip team, are working hard, in their various levels of expertise, to make BrainChip a Global success story.
FabricatedLunacy
Regular
I hope so too... but in 2 years since Sean joined we have not seen any real tangible results. Which I find deeply concerning. I truly hope this is the bottom and all those announcements, and comments on how great Akida is, starts to show in said financials.
DingoBorat
Slim
It's a bit like growing truffles FL (which I'm sure you're familiar with
)..The time put into the groundwork and preparation, takes more time than you would think, but the end results are well worth it (apparently
..).
GazDix
Regular
As usual, here are the changes to the top 20 from the 2nd to 3rd quarter:
1. PVDM (founder) - same
2. Citi - 113 million down from 156 million. Massive drop of 43 million shares or 38%.
3. BNP - up about 6 million shares (second biggest accumulators in last two quarters).
4. Merril - up around 100k shares (they have been around this amount all year give or take).
5. JP Morgan - up 11 million shares. They bought over 20 million shares last two 'terrible' quarters. Biggest accumulators on the dip.
6. HSBC Australia - down 15 million shares.
7. BNP (2) - up 4 million.
8. Certane - up 10 million.
9. LDA - same.
10. HSBC (2). down 12 million.
11. BNP (3) - up 1 million.
12. National Nominees - down 7 million.
13. Osserian Fam - same
14. Crossfield - same
15. Certane - up one million
16. Finclear (new) - hold 6.8 million shares. No idea how many they bought.
17. Paul (retail) - same.
18. Warbont nominees - down 2.2 million.
19. Jeff (retail) - same
20. David (retail) - same
Lou Di Nardo (former CEO) and Superhero are out of the top 20.
Interesting changes nonetheless.
Best wishes all, onwards and upwards.
1. PVDM (founder) - same
2. Citi - 113 million down from 156 million. Massive drop of 43 million shares or 38%.
3. BNP - up about 6 million shares (second biggest accumulators in last two quarters).
4. Merril - up around 100k shares (they have been around this amount all year give or take).
5. JP Morgan - up 11 million shares. They bought over 20 million shares last two 'terrible' quarters. Biggest accumulators on the dip.
6. HSBC Australia - down 15 million shares.
7. BNP (2) - up 4 million.
8. Certane - up 10 million.
9. LDA - same.
10. HSBC (2). down 12 million.
11. BNP (3) - up 1 million.
12. National Nominees - down 7 million.
13. Osserian Fam - same
14. Crossfield - same
15. Certane - up one million
16. Finclear (new) - hold 6.8 million shares. No idea how many they bought.
17. Paul (retail) - same.
18. Warbont nominees - down 2.2 million.
19. Jeff (retail) - same
20. David (retail) - same
Lou Di Nardo (former CEO) and Superhero are out of the top 20.
Interesting changes nonetheless.
Best wishes all, onwards and upwards.
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