BRN Discussion Ongoing

[Space Cadet]

You cannot compare salaries of MDs and CEOs of US based companies to those of Aus based companies.
Sean as part of his package gets circa $US500K cash and the rest as shares and options (81%).
$US500K cash for a CEO is peanuts in the US.
If BRN goes belly up and his shares worthless he has worked for Jack Shiete.
His package is effectively designed to reward performance.
If his 5 year plan plays out he will be very, very wealthy via his package with 81% equities.
If you look at the recent Ann concerning Tony Viana share sales you will see 250k of restricted shares have vested and therefore attract income tax..
Note restricted shares for Tony after the transaction has reduced by the 250k vested shares.
Note that he sold 85k of those 250k shares at 23.5 cents to pay income tax on those 250k vested/he now owns shares.
The net result is that he owns out right an extra 165k shares (250k less 85k sold). The increase in his holding is reflected in the ann.
So in effect Tony has via his salary package purchased an extra 165k shares.
Contrary to what the downrampers on the crapper say these shares are not free as they are paid for via pay package.
Shares as part of a salary package are a great incentive strategy.
The irony is if you believe the company is a dud then senior staff are actually being paid dud money.
If you believe the company will eventually succeed then senior staff will be very, very well off financially via their holdings.
The bottom line is that the BOD is accumulating.

Well said. Also opportunities for them to buy shares is limited especially if they are in possession of knowledge that would be seen as insider knowledge. I'm hoping they are in possession of a shit load of that knowledge.

SC

 

[manny100]

Well said. Also opportunities for them to buy shares is limited especially if they are in possession of knowledge that would be seen as insider knowledge. I'm hoping they are in possession of a shit load of that knowledge.

SC

Sean is all in up to his neck financially with BRN.
When Sean talks BRN up he is backing it up with his hard earned.
You are right, Sean taking 81% of his salary as equity avoids any 'inside trading' allegations/problems.
This applies to the BOD who are net accumulators of equity.

 

[Frangipani]

Intriguing interview with Lavinia Andreea Danielescu, Director of the Future Technologies R&D group at Accenture Labs and one of the co-inventors of the Accenture patents “Neuromorphic smooth control of robotic arms” (filed on 24 May 2022, granted on 27 August 2024), “Self-learning neuromorphic acoustic model for speech recognition” (filed on 16 September 2022, granted on 12 November 2024) and “Self-learning neuromorphic gesture recognition models” (filed on 22 November 2022, application first published on 23 May 2024), all three of which mention both Akida and Loihi as examples of neuromorphic processors:

Interview Of The Week: Andreea Danielescu, Future Technologies Expert - The Innovator

Andreea Danielescu speaks to The Innovator about emerging technology trends and how to prepare for the future.

theinnovator.news

Latest articles

Interview Of The Week: Andreea Danielescu, Future Technologies Expert​

9 hours ago
by Jennifer L. Schenker
7 min read

Written by Jennifer L. Schenker

Andreea Danielescu is the Director of the Future Technologies R&D group at Accenture Labs. Her group focuses on new emerging technologies that blend the physical and digital, including biotechnology, smart materials, energy harvesting and storage and neuromorphic computing. Her specific areas of expertise also include conversational and gestural interfaces, wearable technologies, and AI and tech ethics. Prior to Accenture she worked as an engineer and researcher on conversational interfaces at both Facebook and Intel. Danielescu received her bachelor’s in computer science and mathematics from University of Arizona and her PhD from Arizona State University in computer science with an arts, media, and engineering concentration. She holds over 10 patents, has over 50 peer reviewed publications and is a Senior Member of the ACM, a digital library that serves as a research, discovery and networking platform for computing educators, researchers, and professionals. Danielescu, a speaker at the XPANSE conference in Abu Dhabi Nov. 20-22, recently spoke to The Innovator about emerging technology trends and how to prepare for the future.

Q: Tell us about Accenture’s Future Technologies R&D group.

AD: The group is currently focusing on neuromorphic computing [an approach to computing that uses physical artificial neurons to do computations, mimicking the human brain], smart materials and biomaterials. We work on Edge-based solutions that are low power, energy harnessing solutions and smart materials. It is part of a larger focus on sustainability. A lot of our clients are looking for alternatives to plastics and new packaging materials. We think about new designs, such as finding an alternative to glucose monitors or diabetic pumps that need to be returned for disassembly. We don’t have our own materials lab. We work a lot with university partners and run about 2 dozen projects at any given time.

Q: Can you give some examples of your projects?

AD: One example is dissolvable, degradable electronics. Advances in manufacturing methods for electronics most often aim to produce highly integrated and reliable devices for long-term use. While these features have brought benefits to customers, they also have many side effects. One of these is e-waste, which is the fastest-growing waste stream in the world. Significant effort has been put toward e-waste recycling, but the composition of electronic devices makes the recycling process far more challenging than that of other materials like metals and cardboard. This is exacerbated by the increasing rate at which we produce smart devices, leading to much more e-waste than today’s recycling methods can handle.

In our work, we investigated how materials, fabrication tools and methods, and different types of destruction (melting, dissolving, etc.) can be combined to make devices with sustainability, transiency, and interactivity at the core. We worked on three practical approaches for building such devices including: laser-transferring edible gold foil onto 3D printed chocolates, inkjet printing conductive traces on water-soluble PVA sheets and fabricating electronic devices using natural beeswax.

An example I discussed during a panel at EXPANSE, is a seed carrier that can be dropped from drones and drill seeds into the soil to help with reforestation in areas where it is difficult or dangerous for humans to reach.

Another project involved leveraging the inherent material properties of natural materials, such as paper, leaf skeletons, and chitosan, along with silver nanowires, to create a new decomposable portable heater capable of being electrically controlled. This leaf powered heater can reach temperatures of >70°C and is flexible, lightweight, low-cost, and reusable. Use cases include heating snacks or beverages on the go or simple heating of wax strips for hair removal.

We are currently working on ways to create sensors out of textiles. Everything from sensing and actuation to the power and communications can be textile-based. The next step will be textile-based intelligence. Applications include biometrics for health and wellness embedded in your clothing. The more you know about your body the better off you are, MXenes, which were discovered in 2011 by Yury Gogotsi at Drexel University are finally approaching enough maturity to provide practical solutions to e-textiles that weren’t possible with only conductive copper or silver yarns. By creating fully textile-based systems you also eliminate the filaure points of hard (traditional electronics) to soft (textile) connections that have made many e-textiles impractical in the past.

Q: What changes do you see coming?

AD: In our conversations with clients, academia and the industry about what is top of mind we start to see trends and understand what people are worried about now. How will this change the market? Supply chain management and product authenticity for high-end goods and how we think about counterfeits need to be rethought.

This is not just a technology question. Ensuring ethical practices throughout the supply chain is dependent on humans. Unless multiple parties that don’t know each other have a vested interest in a particular outcome you will never have a neutral outcome. Tracking supply chains relies on a reporting structure that is reliant on people with vested interests, so it is a social problem just as much as a technological one.

More and more consumers are demanding ethically and sustainably sourced goods. How do you provide that information to your clients? How do you develop interactive elements into a product that can give detailed information? This all goes hand-in-hand with tracking things on the supply chain. We will see more and more companies adding interactive elements into products. An example of this would be a smart label on a wine bottle that uses near-field communication (NFC) on mobile phones to access a website about that wine, giving consumers much more information without adding package. An edible RFID sensor can be added to chocolate that allows consumers to scan it with their phone to enter a sweepstakes and win a ticket to an event.

NFC is built into everything so we can do this with what we have out there already. Printing gold leaf onto a chocolate bar can be done using very cheap materials that are readily available. Ubiquitous computing is already here. We just need to leverage what is already out there and add a little bit more.

Privacy and personalization will also become more important. Neuomorphic computing uses low latency and allows privacy preserving computing at the edge. If you don’t need to send information to the Cloud you can safeguard privacy and this will increase opportunities for personalized services. The ability to create a digital signature on textiles by varying pressure and tempo can provide solutions that are more robust to counterfeiting by providing more biomarkers for each person’s signature than is available today through touch screens.

As we reduce power usage requirements we can move more computational systems into the environment, improving efficiency and convenience while also protecting privacy. For example, low-power sensors can be used to structural health monitoring, providing early warnings of bridge failures. Low cost, degradable sensors can be used to help with crop management by providing real-time updates on soil and plant health and disease, allowing farmers to respond more quickly to changing conditions and increase the likelihood of good crop yields.

Personalization can also be applied to communication. The voices of digital assistants or on text-to-speech devices will be customized so that people – whether they are male or female or non-binary -can find voices that sound like them.

Q: Based on your experience, what is the best way for corporates to anticipate and prepare for the future?

AD: To prepare for the future you need dedicated resources. It can be challenging to justify but you need to properly resource a future-facing division and hire the right people. An interdisciplinary team is critical to this. R&D requires much longer cycles, on the order of three-to-five years minimum to explore a new area and start to develop applications and methods to scale the technology. Technology forecasting services can be helpful. Conversations with a wide range of researchers, clients, and subject matter experts in the industries of interest are also critical to identifying potential problems that could benefit from a novel solution and emerging technologies that are worth exploring. . R &D requires much longer cycles.

About the author​

Jennifer L. Schenker​

Jennifer L. Schenker, an award-winning journalist, has been covering the global tech industry from Europe since 1985, working full-time, at various points in her career for the Wall Street Journal Europe, Time Magazine, International Herald Tribune, Red Herring and BusinessWeek. She is currently the editor-in-chief of The Innovator, an English-language global publication about the digital transformation of business. Jennifer was voted one of the 50 most inspiring women in technology in Europe in 2015 and 2016 and was named by Forbes Magazine in 2018 as one of the 30 women leaders disrupting tech in France. She has been a World Economic Forum Tech Pioneers judge for 20 years. She lives in Paris and has dual U.S. and French citizenship.

 

[Bravo]

New AI Algorithms Streamline Data Processing for Space-based Instruments | Science Mission Directorate

A team of NASA personnel and contractors has prototyped a new set of algorithms that will enable instruments in space to process data more efficiently. Using these algorithms, space-based remote sensors will be able to provide the most important data to scientists on the ground more quickly and...

science.nasa.gov

​

SNAPSHOT​

A team of NASA personnel and contractors has prototyped a new set of algorithms that will enable instruments in space to process data more efficiently. Using these algorithms, space-based remote sensors will be able to provide the most important data to scientists on the ground more quickly and may also be able to autonomously determine which Earth phenomena are the most important to observe.

The International Space Station, where Steve Chien and his team prototyped a new set of AI algorithms that will reduce data latency and improve dynamic targeting capabilities for satellites. (Credit: NASA/ISS)
Earth-observing instruments can gather a world’s worth of information each day. But transforming that raw data into actionable knowledge is a challenging task, especially when instruments have to decide for themselves which data points are most important.

“There are volcanic eruptions, wildfires, flooding, harmful algal blooms, dramatic snowfalls, and if we could automatically react to them, we could observe them better and help make the world safer for humans,” said Steve Chien, a JPL Fellow and Head of Artificial Intelligence at NASA’s Jet Propulsion Laboratory.

Engineers and researchers from JPL and the companies Qualcomm and Ubotica are developing a set of AI algorithms that could help future space missions process raw data more efficiently. These AI algorithms allow instruments to identify, process, and downlink prioritized information automatically, reducing the amount of time it would take to get information about events like a volcanic eruption from space-based instruments to scientists on the ground.

These AI algorithms could help space-based remote sensors make independent decisions about which Earth phenomena are most important to observe, such as wildfires.

“It’s very difficult to direct a spacecraft when we’re not in contact with it, which is the vast majority of the time. We want these instruments to respond to interesting features automatically,” said Chien

Chien prototyped the algorithms using commercially available advanced computers onboard the International Space Station (ISS). During several different experiments, Chien and his team investigated how well the algorithms ran on Hewlett Packard Enterprise’s Spaceborne Computer-2 (SBC-2), a traditional rack server computer, as well as on embedded computers.

These embedded computers include the Snapdragon 855 processor, previously used in cell phones and cars, and the Myriad X processor, which has been used in terrestrial drones and low Earth orbit satellites.

Including ground tests using PPC-750 and Sabertooth processors – which are traditional spacecraft processors – these experiments validated more than 50 image processing, image analysis, and response scheduling AI software modules.

The experiments showed these embedded commercial processors are very suitable for space-based remote sensing, which will make it much easier for other scientists and engineers to integrate the processors and AI algorithms into new missions.

The full results of these experiments were published in a series of three papers at the 2022 IEEE Geoscience and Remote Sensing Symposium, which can be accessed through the links below.

Chien explains that while it is easiest to deploy AI algorithms from ground computers to larger, rack-mounted servers like the SBC-2, satellites and rovers have less space and power, which means they would need to use smaller, low-power, embedded processors similar to the Snapdragon or Myriad units.

By processing the data onboard, these AI algorithms prevent important or urgent information from being buried within larger data transmissions. A researcher wouldn’t have to downlink and process an entire transmission to see that a hurricane is intensifying or a harmful algal bloom has formed.

“A large image could have gigabytes of data, so it might take a day to get it to the ground and process it. But you don’t need to process all that data to identify a wildfire. These algorithms pre-process data onboard so that researchers get the most important information first,” said Chien.

These algorithms could be useful not only for Earth-observing instruments, but also for instruments observing other planets as well. The proposed Europa Lander mission, for example, could use Chien’s algorithms to help search for life on the Jovian moon.

“There are several missions that are in concept development right now that could use this technology. They’re still in the early phases of development, but these are missions that need the kind of onboard analysis, understanding, and response these algorithms enable,” said Chien.

The team is also testing neural network models to interpret Mars satellite imagery. “Someday such a neural net could enable a satellite to detect a new impact ejecta, evidence of a meteorite impact, and alert other spacecraft or take follow-up images,” said JPL Data Scientist Emily Dunkel. “Rovers could also use these processors with neural networks to determine where it is safe for the rover to drive,” Dr. Dunkel added.

“We used the CogniSat framework to deploy models to the Myriad X, reducing the effort to develop deep learning models for onboard use. This experience helps prove that this advanced hardware and software system is ready now for space missions,” said Léonie Buckley, Senior Engineer at Ubotica.

As climate change continues to alter the world we live in, information systems like Chien’s allow scientific instruments to be as dynamic as the Earth systems they observe.

“We don’t often think about the fact that we’re walking around with more computing power in our cell phones than supercomputers had forty years ago. It’s an amazing world we live in, and we’re trying to incorporate those advancements into NASA missions,” said Chien. View attachment 35765 View attachment 35765

Following on from @Tothemoon24 's post, which described how NASA’s JPL (Jet Propulsion Laboratory) was working with Qualcomm and Ubotica on developing a set of AI algorithms that could help future space missions process raw data more efficiently to detect events like a volcanic eruptions, wildfires, flooding, harmful algal blooms, dramatic snowfalls, etc.

Well, I was just reading this article below dated 14 June 2024, and it talks about Ubotica having been awarded Horizon Europe funding through the "Meseo project", which aims to revolutionise Earth observation systems. The project is a collaboration between various companies including GMV and Airbus!

In trying to get to the bottom of whether we are linked to the work with NASA JPL, Qualcom and Ubotica, I came upon this research paper dated October 2023, which has probably been posted in TSEx previously. The paper titled "NimbleAI: 3D-Integrated Neuromorphic Vision Sensor-Processor" was written by Xabier Iturbe , Gianluca Furano (ESA) and Didier Keymeulen (NASA JPL), and it talks about using SNN's to help monitor the earth's surface for phenomena such as explosive eruptions (think volcanoes) and the like.

It's interesting because back in March 2024, one of the authors of this paper, Xabier Iturbe, liked a post about BrainChip's Edge Box on LinkedIn (see below).

Anyway, if you take a look at the MESEO Projects website, you can see Airbus is listed as a partner! And it makes me wonder whether the agreement we signed with Airbus could also have something to do with the MESEO project.

It's a bit unfortunate that I only just discovered this website today because it says they held a Webinar on the 28th of November, which would have been great to tune into to check for any other potential signs of our involvement.

https://www.researchgate.net/publication/377652579_NimbleAI_3D-Integrated_Neuromorphic_Vision_Sensor-Processor_to_go_where_our_Eyes_can't

Dublin’s Ubotica joins EU project to boost Earth observation​

by Leigh Mc Gowran
14 Jun 2024
Save article

Image: © Dabarti/Stock.adobe.com
The Meseo project aims to create a system that can support the vast amount of data that satellites transfer, to improve various Earth observation services.
Irish space-tech company Ubotica has been awarded Horizon Europe funding through the Meseo project, which aims to revolutionise Earth observation systems.
Meseo is a collaborative research project that aims to make Europe’s space sector more competitive. The Horizon Europe-backed project aims to develop a scalable multi-mission Earth observation system that can support large-scale data processing.
This system will be designed to manage and process the huge amount of data that numerous satellites must transfer. The aims to reduce communication bandwidth requirements and utilise advanced technologies to optimise power consumption, processing capabilities and system usability.
The heart of this project is an Earth observation coordination centre, where specific software called processing functions will manage satellite data and products, to guarantee their ownership and quality. The goal is to create an accessible ecosystem that is open to any Earth observation product and service.
The project is a collaboration between various companies including GMV and Airbus. Ubotica is responsible for the development and deployment of AI-driven image triaging and in-line processing.
“We are excited to contribute to Meseo’s ambitious objectives and we look forward to the innovative advancements this partnership will bring to the space industry,” said Dr Aubrey Dunne, Ubotica co-founder and CTO. “Our AI-driven solutions are poised to revolutionise how data is processed on board, enhancing overall efficiency and effectiveness, and the Meseo system will significantly benefit from the integration of these solutions.”
Founded in 2017 and based at Dublin City University, Ubotica has had exciting developments in recent years, including striking a partnership with IBM and forming a corporate entity in the US to expand its presence in the country.

Earlier this year, Ubotica successfully launched its CogniSAT-6 satellite, as part of its mission to improve Earth observation services. This satellite is designed to provide real-time data to support various activities, such as monitoring crop health or tracking illegal fishing.

Dublin’s Ubotica joins EU project to boost Earth observation

Irish space-tech company Ubotica has joined the Meseo project, which is being funded by Horizon Europe to revolutionise Earth observation.

www.siliconrepublic.com

Home - MESEO

MESEO will design, prototype and demonstrate an open, flexible and scalable multi-mission End-to-End Earth Observation (EO) system for massive data...

meseoproject.eu

 

[Bravo]

And do you know what else is pretty cool?!!!

It's that Xabier Iturbe (Nimble AI's project co-ordinator), the same guy that liked the Linkedin post about BrainChip's Edge Box in March 2024 (see above), just posted this on LinkedIn a mere 9 hours ago.

I love some of the quotes included here!

"We expect up to 57% penetration of neuromorphic chips in most major applications by 2034" - YOLE Intelligence

"Neuromorphic computing will have a substantial impact on existing products and markets, taking three to six years to cross over from early-adopter status to early majority adoption" - Gartner.

I checked where this Gartner quote first came from and it came from an article dated 19 January 2023, so it will now be roughly "two to five years from early adoption to majority adoption".

Xabier Iturbe, PhD on LinkedIn: AESEMI_neuromorphic_leaflet

👋 Nos vemos este lunes en #Chipnation! Presentaremos los primeros resultados del grupo de trabajo en tecnologías neuromórficas e IA de la Asociación Española…

www.linkedin.com

January 19, 2023

Contributor: Tuong Nguyen

The 2023 Gartner Emerging Technologies and Trends Impact Radar shows product leaders where to capitalize on market opportunities.

Gartner research reveals four emerging technologies and trends to which tech vendors and product leaders will need to respond, calibrating their tech strategies, investments and tools to stay ahead:

1. Smart world expands with increased fusion of physical-digital experiences.
2. Productivity revolution accelerates with advances in artificial intelligence (AI) tools and tech.
3. Transparency and privacy get more scrutiny amid exponential growth in corporate and personal data collection.
4. New critical technology enablers create new business and monetization opportunities.

What’s on the 2023 Gartner Emerging Technologies and Trends Impact Radar?​

These trends surfaced in our 2023 Gartner Emerging Technologies and Trends Impact Radar, which highlights 26 emerging trends and technologies to which vendors must respond, whether they are a new or established player in that space.

The Impact Radar portrays the maturity, market momentum and influence of technologies, making it a handy tool for product leaders to identify and track the technologies and trends that will help them improve and differentiate their products, remain competitive and capitalize on market opportunities.

Download now: Your Detailed Guide to Gartner Emerging Tech Impact Radar 2023

Four Emerging Technologies Disrupting the Next Three to Eight Years​

Most of this year’s emerging technologies and trends are three to eight years away from reaching widespread adoption but represent significant innovation in the years ahead.

Let’s look at four we think will prove especially interesting.

No. 1: Neuromorphic computing​

• A critical enabler, neuromorphic computing provides a mechanism to more accurately model the operation of a biological brain using digital or analog processing techniques.
• It will take three to six years to cross over from early-adopter status to early majority adoption.
• Neuromorphic computing will have a substantial impact on existing products and markets.

Neuromorphic computing systems simplify product development, enabling product leaders to develop AI systems that can better respond to the unpredictability of the real world. Their autonomous capabilities quickly react to real-time events and information, and will form the basis of a wide range of future AI-based products. Early use cases include event detection, pattern recognition and small dataset training.

We expect breakthrough neuromorphic devices by the end of 2023, but it will likely take five years for these devices to reach early majority adoption.

The impact is likely to be significant, though, as neuromorphic computing is expected to disrupt many of the current AI technology developments, delivering power savings and performance benefits not achievable with current generations of AI chips.

No. 2: Self-supervised learning​

• Self-supervised learning accelerates productivity by using an automated approach to annotating and labeling data.
• It will take six to eight years to cross over from early-adopter status to early majority adoption.
• Self-supervised learning will have a significant impact on existing products and markets.

Self-supervised models learn how information relates to other information; for example, which situations typically precede or follow another, and which words often go together.

Self-supervised learning has only recently emerged from academia and is currently practiced by a limited number of AI companies. A few companies focused on computer vision and NLP products have recently added self-supervised learning to their product roadmaps, however.

The potential impact and benefits of self-supervised learning are extensive, as it will extend the applicability of machine learning to organizations with limited access to large datasets. Its relevance is most prominent in AI applications that typically rely on labeled data, primarily computer vision and NLP.

No. 3: Metaverse​

• The metaverse fuels the smart world by providing an immersive digital environment.
• It will take eight-plus years to cross over from early-adopter status to early majority adoption.
• The metaverse will have a very substantial impact on existing products and markets.

The metaverse enables persistent, decentralized, collaborative, interoperable digital content that intersects with the physical world’s real-time, spatially organized and indexed content.

It is an example of a combinatorial trend in which a number of individually important, discrete and independently evolving trends and technologies interact with one another to give rise to another trend. The emerging, supporting technologies and trends include (but are not limited to) spatial computing and the spatial web; digital persistence; multientity environments; decentralization tech; high-speed, low-latency networking; sensing technologies; and AI applications.

The features and functionality these ETT bring to the metaverse will need to reach an early majority in order for the metaverse to cross the chasm. We consider all current examples to be precursors or premetaverse offerings because they are potentially capable and compatible but do not yet meet the definition of the metaverse.

While the benefits and opportunities from the metaverse are not immediately viable, emerging metaverse solutions give an indicator of potential use cases. We expect the transition toward the metaverse to be as significant as the one from analog to digital.

No. 4: Human-centered AI​

• Human-centered AI (HCAI) is a common AI design principle calling for AI to benefit people and society, which could improve transparency and privacy.
• It will take three to six years to reach early majority adoption.
• HCAI will have a substantial impact on existing products and markets.

HCAI assumes a partnership model of people and AI working together to enhance cognitive performance, including learning, decision making and new experiences. HCAI is sometimes referred to as “augmented intelligence,” “centaur intelligence” or “human in the loop,” but in a wider sense, even a fully automated system must have human benefits as a goal.

HCAI enables vendors to manage AI risks, and to be ethical, responsible and more efficient with automation, while complementing AI with a human touch and with common sense. Many AI vendors have already shifted their positions to the more impactful and responsible HCAI approach. The technology-centric approach of developing AI products has led to numerous negative impacts, urging vendors to rethink their AI product strategies.

The potential impact of HCAI is high because it leverages human abilities to make humans more productive and remove avoidable limitations, biases and blind spots.

In short:

• The Gartner Emerging Tech Impact Radar highlights the technologies and trends that have the most potential to disrupt a broad cross section of markets.
• The trends are organized around four key themes, which are critical for product leaders to evaluate as part of their competitive strategy.
• Product leaders must explore these technologies now to capitalize on market opportunities.

Tuong Nguyen is a Director Analyst within the Emerging Technologies and Trends team in Gartner Research. He undertakes analysis on immersive technologies, metaverse, computer vision, SLAM and human-machine interfaces. He advises tech provider product leaders how to factor emerging tech and trends into creating and evolving highly successful product offerings.

Gartner Tech Growth & Innovation Conference​

Join the world’s leading IT and business leaders to get an update on accelerating tech growth in a new era of transformation and technology trends.

4 Emerging Technologies You Need to Know About

January 19, 2023 Contributor: Tuong Nguyen The 2023 Gartner Emerging Technologies and Trends Impact Radar shows product leaders where to capitalize o

gartner.eu

 

[Pom down under]

IP announcement, next week for sure folks!

Come on spill the beans and let us know what asx company I should be putting some money on Monday?

 

[Diogenese]

delayed reaction:

A coupe of years ago there was talk of potential patent litigation between iniVation and Prophesee.

Does the merger of iniVation and SynSense preclude further cooperation between Prophesee and SynSense?

https://www.synsense.ai/synsense-an...orm-leading-neuromorphic-technology-provider/

SynSense and iniVation join forces to form leading neuromorphic technology provider​

2024-02-01

Who would Prophesee turn to for NN if this is the case?

I'd thought that Prophesee had deveoped its low-fi 320*320 pixel array so as not to overstretch SynSense capabilities.

https://brainchip.com/brainchip-introduces-second-generation-akida-platform/

BrainChip Introduces Second-Generation Akida Platform​

Introduces Vision Transformers and Spatial-Temporal Convolution for radically fast, hyper-efficient and secure Edge AIoT products, untethered from the cloud
Laguna Hills, Calif. – March 6, 2023
...
“At Prophesee, we are driven by the pursuit of groundbreaking innovation addressing event-based vision solutions. Combining our highly efficient neuromorphic-enabled Metavision sensing approach with Brainchip’s Akida neuromorphic processor holds great potential for developers of high-performance, low-power Edge AI applications. We value our partnership with BrainChip and look forward to getting started with their 2nd generation Akida platform, supporting vision transformers and TENNs,” said Luca Verre, Co-Founder and CEO at Prophesee.

Luca Verre, Co-Founder and CEO,Prophesee

Interestingly, on their ML page, Prophesee have a number of linked pages, including a password protected page from July 2023.
https://www.prophesee.ai/?s=machine+learning

Protected: Protected: DRAFT Metavision® Intelligence – Event-Based Vision Software​

Jul 18, 2023

Password Protected​

This is 4 months after Luca's comment about TENNs was published. NDA?

 

[Tothemoon24]

Faster neuron-style processing to be capable of lightning-quick processing while using 90 per cent power
Mercedes-Benz says it’s being forced to explore new ways of crunching huge amounts of data as it ramps up autonomous driving technology because its current processers are too slow and consume too much energy.
Existing Level 2 adaptive cruise control uses between 70-100W of energy, according to the German car-maker, but the more sophisticated Level 3 cruise, introduced on its latest S-Class, consumes around 400W.
The next-generation Level 4 driverless aids sucks-up as much as 3000W in operation while the final Level 5 being developed to see cars, vans and trucks operate without any human involvement at all will use as much as 20kW of power – and that’s not sustainable on its future electric vehicles.

This has forced engineers to look elsewhere for its solution to more efficient computing.
“The most efficient computer processing unit we know is the brain. As always, biology provides the answer, for a human to carry out the equivalent of L5 autonomy the brain consumes just 20W of power – a fraction of what we were achieving in the lab,” a Mercedes scientist told carsales.
Mercedes will eventually use new-generation neuromorphic computers – currently being developed – that are not only better and react faster are 10 times more efficient than current systems.

Sadly, to fully understand the tech needs a doctorate but the most basic explanation is that it uses silicon neurons to form a neural network that mimics the brain.
While existing tech wastes time processing everything a camera ‘sees’, the silicon neurons only react to spikes of information and the resulting event-triggered computation is both quicker and more efficient by up to 90 per cent in its current use, compared to existing tech.

 

[Bravo]

delayed reaction:

A coupe of years ago there was talk of potential patent litigation between iniVation and Prophesee.

Does the merger of iniVation and SynSense preclude further cooperation between Prophesee and SynSense?

https://www.synsense.ai/synsense-an...orm-leading-neuromorphic-technology-provider/

SynSense and iniVation join forces to form leading neuromorphic technology provider​

2024-02-01

Who would Prophesee turn to for NN if this is the case?

I'd thought that Prophesee had deveoped its low-fi 320*320 pixel array so as not to overstretch SynSense capabilities.

https://brainchip.com/brainchip-introduces-second-generation-akida-platform/

BrainChip Introduces Second-Generation Akida Platform​

Introduces Vision Transformers and Spatial-Temporal Convolution for radically fast, hyper-efficient and secure Edge AIoT products, untethered from the cloud
Laguna Hills, Calif. – March 6, 2023
...
“At Prophesee, we are driven by the pursuit of groundbreaking innovation addressing event-based vision solutions. Combining our highly efficient neuromorphic-enabled Metavision sensing approach with Brainchip’s Akida neuromorphic processor holds great potential for developers of high-performance, low-power Edge AI applications. We value our partnership with BrainChip and look forward to getting started with their 2nd generation Akida platform, supporting vision transformers and TENNs,” said Luca Verre, Co-Founder and CEO at Prophesee.

Luca Verre, Co-Founder and CEO,Prophesee

Interestingly, on their ML page, Prophesee have a number of linked pages, including a password protected page from July 2023.
https://www.prophesee.ai/?s=machine+learning

Protected: Protected: DRAFT Metavision® Intelligence – Event-Based Vision Software​

Jul 18, 2023

Password Protected​

This is 4 months after Luca's comment about TENNs was published. NDA?

Hi Diogense,

Wow, great point!

The other thing that's really good for us is the time-line of events that were involved. Just to recap, SynSense and Prophesee announced their partnership on October 15, 2021. But then on the podcast in March 2023 with Rob Telson and Luca Verre, Luca spoke in glowing terms about BrainChip.

If you listen around the 26 minute mark, Luca talks about how BrainChip and Prophesee are natural partners because they both have very complementary technologies. He then recalls just how excited Christoph was, saying that beforehand they only had half the story and now they can tell their customers the full story. Luca also said because of this they can now push offerings to their customers to unprecedented levels in their industry.

I can't imagine Synsense being very happy hearing Luca waxing lyrical about BrainChip in this manner.

 

[Bravo]

That's a great point @manny100.

It might be a simplistic way of looking at things, but for me it boils down to two particular areas:

• performance and efficiency calculated by TOPS/watt, and
• the unique processing capabilities that BrainChip's technology can bring to the table

If BrainChip's technology can deliver on both of these areas in a meaningful way, then there should be no reason for Qualcomm not to want to integrate it into their products. As has been mentioned numerous times, we are trying to position ourselves as a partner rather than a competitor to behemoths like Qualcomm and NVIDIA.

In terms of determining the TOPS/watt capabilities of Qualcomm's chips, it's a bit tricky because there doesn't seem to be any specified values from the manufacturer on power consumption measurements. In this instance, the authors of one article (published 20 June 2024, linked below) estimated via their own power consumption measurements that "the most efficient power range of the Snapdragon X Elite chips seems to be 20-30 Watts".

AKIDA's Pico on the other hand operates in the microwatt (μW) to milliwatt (mW) range.

When it comes to the process that BrainChip's technology allows for, we can look to Max Maxfiled's latest article "Taking the Size and Power of Extreme Edge AI/ML to the Extreme Minimum" dated 21 Nov 2024. The obvious benefit is that you can "feed the event-based data from the camera directly to the event-based Akida processor, thereby cutting latency and power consumption to a minimum", as compared to other available techniques.

The big question is whether Qualcomm would see any value in adopting this type technology into their own products and I think that Judd Heape, VP for product management of camera, computer vision and video at Qualcomm Technologies, might have actually answered that question in an EE Times article dated 22 March 2023 when he stated the following.


EXTRACT - EE Times article dated 22 March 2023 (Interview with Judd Heape, VP for product management of camera, computer vision and video at Qualcomm Technologies).

View attachment 73247





EXTRACT - Notebook Check 20 June 2024
View attachment 73241


EXTRACT - Notebook Check 20 June 2024
View attachment 73244


EXTRACT - EE Journal"Taking the Size and Power of Extreme Edge AI/ML to the Extreme Minimum" 21 Nov 2024.
View attachment 73246


Links

EETimes article about Prophesee-Qualcomm deal

Full article here: https://www.eetimes.com/experts-weigh-impact-of-prophesee-qualcomm-deal/ Experts Weigh Impact of Prophesee-Qualcomm Deal ...

image-sensors-world.blogspot.com

https://www.notebookcheck.net/Qualcomm-Snapdragon-X-Elite-Analysis-More-efficient-than-AMD-Intel-but-Apple-stays-ahead.850221.0.html

Taking the Size and Power of Extreme Edge AI/ML to the Extreme Minimum

Earlier this year, I penned a couple of columns under the umbrella title “Mind-Boggling Neuromorphic Brain Chips.” One of the first comments I received concerning these columns was short, sharp, an…

www.eejournal.com

What's also interesting is that in March 2023 at pretty much the same time that BrainChip and Prophesee recorded this podcast, Judd Heape (VP for product management of camera, computer vision and video at Qualcomm Technologies), was interviewed by EE times on Prophesee and Qualcomm's partnership.

In the article titled "Experts Weigh Impact of Prophesee-Qualcomm Deal" dated 22 March 2023, Judd talked about how Qualcomm are interested in low power use cases like gesture recognition to control the car while your driving (see above post). Can you imagine how much extra power that would consume in a vehicle? So if gesture recognition is to become a reality, then both Prophesee and Qualcomm would be looking for something that isn't just low power, but ULTRA LOW POWER to manage all of that additional processing work I suppose.

EETimes article about Prophesee-Qualcomm deal

Full article here: https://www.eetimes.com/experts-weigh-impact-of-prophesee-qualcomm-deal/ Experts Weigh Impact of Prophesee-Qualcomm Deal ...

image-sensors-world.blogspot.com

 

[Diogenese]

Hi Diogense,

Wow, great point!

The other thing that's really good for us is the time-line of events that were involved. Just to recap, SynSense and Prophesee announced their partnership on October 15, 2021. But then on the podcast in March 2023 with Rob Telson and Luca Verre, Luca spoke in glowing terms about BrainChip.

If you listen around the 26 minute mark, Luca talks about how BrainChip and Prophesee are natural partners because they both have very complementary technologies. He then recalls just how excited Christoph was, saying that beforehand they only had half the story and now they can tell their customers the full story. Luca also said because of this they can now push offerings to their customers to unprecedented levels in their industry.

I can't imagine Synsense being very happy hearing Luca waxing lyrical about BrainChip in this manner.

Yes, once again it's "software". So we have Valeo, Mercedes, and now Prophesee, all three big fans of Akida, using NN software to process sensor signals - and the brightest star in this constellation is TENNs.

As far as energy usage is concerned, TENNs software is a featherweight, It is probably the most efficient AI software in the universe.

We may not see NN hardware in SDVs for a while, but TENNs software has been in the hands of EAps for quite some time now, and each improved version can be instantly provided to the EAPs for testing.

 

[Diogenese]

What's also interesting is that in March 2023 at pretty much the same time that BrainChip and Prophesee recorded this podcast, Judd Heape (VP for product management of camera, computer vision and video at Qualcomm Technologies), was interviewed by EE times on Prophesee and Qualcomm's partnership.

In the article titled "Experts Weigh Impact of Prophesee-Qualcomm Deal" dated 22 March 2023, Judd talked about how Qualcomm are interested in low power use cases like gesture recognition to control the car while your driving (see above post). Can you imagine how much extra power that would consume in a vehicle? So if gesture recognition is to become a reality, then both Prophesee and Qualcomm would be looking for something that isn't just low power, but ULTRA LOW POWER to manage all of that additional processing work I suppose.

EETimes article about Prophesee-Qualcomm deal

Full article here: https://www.eetimes.com/experts-weigh-impact-of-prophesee-qualcomm-deal/ Experts Weigh Impact of Prophesee-Qualcomm Deal ...

image-sensors-world.blogspot.com

Yes, but ... much as I would like to see Qualcomm n our licencee list, Qualcomm have their own in-house digital/analog hybrid compute-in-memory design which they claim has the speed and power efficiency of analog and accuracy of digital. :

US2023297335A1 Hybrid Compute-in-Memory 20220315

A compute-in-memory array is provided that implements a filter for a layer in a neural network. The filter multiplies a plurality of activation bits by a plurality of filter weight bits for each channel in a plurality of channels through a charge accumulation from a plurality of capacitors. The accumulated charge is digitized to provide the output of the filter.

 

[Baisyet]

One of the Partner showcasing in Japan

BeEmotion.ai on LinkedIn: EdgeTech+ 2024

BeEmotion.ai is at EdgeTech+ in Yokohama, Japan. Please talk to our friendly team if you are there!

www.linkedin.com

 

[Diogenese]

One of the Partner showcasing in Japan

BeEmotion.ai on LinkedIn: EdgeTech+ 2024

BeEmotion.ai is at EdgeTech+ in Yokohama, Japan. Please talk to our friendly team if you are there!

www.linkedin.com

You're too modest:
"
The BeEmotion.ai ( https://www.beemotion.ai/ ) Japan team is excited to showcase our Deep Edge AI at the EdgeTech+ ( https://lnkd.in/g38-ireU ) exhibition in Pacifico Yokohama this week. From today, Wednesday, through Friday, we will be demonstrating Interior Monitoring Solutions for Smart Vehicles and highlighting the enhanced capabilities of our algorithms utilizing Brainchip's Akida neuromorphic IP ( https://brainchip.com/

"

 

[Diogenese]

Intel, Qualcomm, and AMD are now providing PC/Laptop processors with NPUs.

This web page lists numerous Laptops using Qualcomm Snapdragom Hexagon X NPU, including Dell, Asus, MS; Lenovo, Samsung, HP:

https://www.microsoft.com/en-us/windows/copilot-plus-pcs?r=1

See also:

https://www.qualcomm.com/products/mobile/snapdragon/laptops-and-tablets/snapdragon-x-elite

https://www.windowscentral.com/hardware/laptops/best-ai-pc

 

[Humble Genius]

Chetan Kadway on LinkedIn: #icpr

Our work that explores learning at the edge on BrainChip Akida Neuromorphic processor got accepted at #ICPR 2024. I would like to congratulate & thank Ajoy Dey…

www.linkedin.com

 

[JB49]

View attachment 73610

Chetan Kadway on LinkedIn: #icpr

Our work that explores learning at the edge on BrainChip Akida Neuromorphic processor got accepted at #ICPR 2024. I would like to congratulate & thank Ajoy Dey…

www.linkedin.com

Looks like Tata will be driving Akida into a lot more than just medical devices!

 

[Luppo71]

If some are wondering why BrainChip doesn’t communicate externally as actively as some might wish, hoping this would suddenly push the stock to $20 per share and skyrocket its valuation, consider this: if something like that were to happen, the question would be whether a small company like BrainChip could handle the enormous pressure of continuously meeting such high expectations to maintain that level. I think it wouldn’t be healthy, as the internal structures are not yet solidified.

Keep in mind that every success also brings additional pressure. Isn’t it much better to focus on organization, improving products, presenting these improvements to potential customers, and building something together calmly and steadily? Rather than constantly promising the moon and delivering nothing in the end.

The company once dared to speak positively about the future, and even that has led to many criticizing the board for allegedly not delivering or doing their job. Quotes like: “Where are the contracts, Sean?” … “Tick-Tock” or “Pack your luggage” are good examples.

I remain confident as there’s no negative news out there. Long-term holder here.

I am not looking for the moon but just something positive on the back of multiple comments from the company.
Nothing more, nothing less.

 

[Bravo]

Samsung sold 1,000 units of its AI washing machine in just three days in South Korea alone – and that’s just the beginning for its energy-conscious appliances​

Features
By Josephine Watson
published 10 hours ago
One small step for washing machines, a giant leap for appliance-kind


When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works.

(Image credit: Samsung)

Samsung has had two major talking points this year; AI and energy efficiency, both of which coalesce in its home appliances and SmartThings technology.

It’s good timing, too. The Center for Law and Social Policy (CLASP), a Washington-based anti-poverty non-profit organization, cites in its Net Zero Heroes report that appliances are responsible for 39 per cent of all energy-related CO2 emissions. With an already immense (and ever-growing) number of devices in use every day, it's a figure that will only increase without some form of change.

There’s no time like the present, then, for manufacturers such as electronics giant Samsung to focus on improving the energy efficiency of its home appliances.

It’s a move that has borne fruit, too, not least in the world of smart washing machines. According to Moohyung Lee, EVP and Head of Customer Experience Team for the Digital Appliances Business at Samsung, the Bespoke AI Laundry Combo sold 1,000 units in just three days when it launched in South Korea in February. By April, it surpassed cumulative sales of 10,000 units, an impressive number for a large appliance.

Now, it’s launching in the US, with further plans for release in South America, Southeast Asia and Europe later this year – and there’s plenty more to get excited about in Samsung’s home appliance roster.

Following the brand’s energy efficiency and AI-first display at IFA 2024, Lee told us more about how Samsung plans to continue its journey towards the smart home of the future.

Clean and energy lean​

Through a combination of software and hardware, Samsung has been enhancing energy efficiency.

Lee cites features like Ecobubble, which has been integral to Samsung washing machines for over a decade. Instead of using heat energy to offer a thorough clean, Ecobubble “turns detergent into bubbles that quickly penetrate the laundry, allowing for effective washing even in cold water, which reduces the energy consumption required to heat water”, he says.
“Ecobubble can be combined with other technologies like Bubble Shot ― which further improves detergent penetration ― to uplift the energy efficiency,” he adds.
There’s also Samsung’s Digital Inverter Technology, found in a variety of appliances from washer motors to refrigerator compressors, and serves to reduce energy wastage. “Not only is it an energy-efficient solution, but it reduces noise and increases durability,” Lee explains. “It reduces unnecessary use of energy by adjusting the components’ rotating speed according to different usage situations.”
On the software side, the SmartThings Energy app allows users to monitor the energy consumption of connected appliances, and also supports AI Energy Mode to automatically optimize energy usage.
“We have various opportunities like partnerships related to carbon intensity or Auto Demand Response (ADR) for example, which enhances the value of SmartThings Energy,” Lee adds
“Looking ahead, Samsung plans to expand service availability from 68 countries at the end of 2023 to 110 markets by the end of 2024, increasing convenience and energy savings for consumers worldwide.”

(Image credit: Samsung)

AI for all​

Samsung’s latest generation of washing machines, fridges and other large appliances also come decked with Bespoke AI, offering a host of clever features and some impressive potential energy-saving chops, too.
“Samsung has introduced various AI-enhanced appliances that give solutions tailored to your needs,” says Lee. “Bespoke Jet Bot Combo AI features AI Floor Detect that senses floor environment with AI, and the refrigerator with AI Family Hub brings AI Vision Inside for better food management for consumers.”
However, it’s the Bespoke AI Laundry Combo that captured homeowners' hearts and minds, by selling those 1,000 units in South Korea over just three days post-launch. “It features AI Wash & Dry, which selects proper washing and drying cycles based on the types of fabric, weight, and soil level,” Lee explains. “Additionally, it features the AI Home, a 7-inch LCD touchscreen, which allows users to intuitively monitor the machine and control various functions.
“One standout feature of AI Home is that it displays the status of SmartThings-connected appliances and gives users the ability to control them remotely.”

Understanding the opportunity​

So, how does Samsung make sure its features and hardware can actually have an impact? According to Lee, the manufacturer uses the “standardized testing protocols” required in each region to meet its energy regulations.
“This includes rigorous testing of our appliances against established energy efficiency standards," he continues. "For features like the AI Energy Mode, we also collaborate with third-party organizations to validate effectiveness and enhance credibility with consumers.”
Samsung focuses not only on energy savings but also considers carbon emissions as an important indicator. “By obtaining the Carbon Trust's Carbon Footprint Certification and participating in the standard for quantifying carbon emissions, it is establishing objective indicators to measure the impact of its products,” explains Lee.
“Samsung is researching and developing technologies to provide solutions that can reduce impact on the environment as much as possible, while also upgrading products and services to make it easier for consumers to participate in this journey. We are applying this vision from the beginning when designing our products.”
This, he explains, can mean anything from developing energy-efficient products, utilizing recycled materials or reusing discarded components. “For example, certain Bespoke stick vacuum cleaner filters use recycled materials from discarded fishing nets, and recycled aluminum is applied to some of Bespoke panels of our refrigerators.”

We all row the boat​

Consumer education plays an important role in making sure these features achieve their full potential. Lee says Samsung encourages consumer participation right from the get-go: “For example, when a product is connected to SmartThings, the app gives users the suggestion to utilise AI Energy Mode.
“Furthermore, AI Energy Mode supports frequently used wash cycles such as Cotton, AI Wash, Synthetics, Mixed Load, and Super Speed to deliver more consistent energy savings. Once a user turns on AI Energy Mode in SmartThings Energy, it works its magic until deactivated, allowing continuous energy saving.”
Once connected, the SmartThings Energy platform can be accessed in myriad ways, whether that’s via on-device screens like the Family Hub or AI Home, using other SmartThings-connected Samsung appliances with a screen or, of course, using a mobile phone.
“Whether it’s simply monitoring energy usage or using AI Energy Mode to reduce additional energy consumption in daily life, consumers can decide how extensively they want to use the platform," says Lee, explaining that users can even choose to set their own goals on a daily, weekly, or monthly basis.
“Samsung designs home appliances not just as functional machines, but as companions that improve and enhance the user experience,” says Lee, a principle that extends to SmartThings Energy. “Moving forward, we’ll continue to provide new ways for users to stay true to their values in daily life.”
The increased focus on efficiency in the home comes at an important crossroads on the road to carbon zero, though, of course, concerns remain about the environmental impact of AI. For now, though, energy-conscious brands like Samsung are at the forefront of using such technology to its advantage – it's not 'just a washing machine' anymore.

Samsung sold 1,000 units of its AI washing machine in just three days in South Korea alone – and that’s just the beginning for its energy-conscious appliances

One small step for washing machines, a giant leap for appliance-kind

www.techradar.com

 

[Bravo]

Energy is Everything for Edge Computing​

By Brandon Lucia, CEO & Co-Founder, Efficient 11.28.2024 0


The need for intelligence in the physical world is pushing more sophisticated computation into edge devices. These devices, which previously were simple conduits between a sensor and the cloud, now support complex AI and ML, digital signal processing, data analytics, radio frequency (RF) data processing, and a host of other use cases.

Pushing intelligence into these devices increases the energy demands of these devices, which are already energy-starved in their widespread and far-reaching deployments. The critical need for energy-efficiency requires fundamentally rethinking the computing hardware that we use to build these devices.

At the heart of most edge computing devices, you will find the computing hardware of yesterday—CPUs and FPGAs—which are inefficient and inflexible. Many devices rely on traditional “von Neumann” processors, which waste as much as 90-99% of the energy that they consume due to architectural inefficiencies, leading to needless data movement and instruction control overheads.
While virtuously programmable, traditional “von Neumann” CPUs are just too inefficient. FPGAs are often the first to replace a CPU at the edge, offering a path away from some of the overheads of the CPU and, in some cases, providing improved performance and efficiency. FPGAs, however, are a challenging target for application developers, requiring the specialized skills of a digital design team and a much longer time to market. Moreover, FPGAs were originally designed for circuit simulation and are overspecialized for inessential features, yet under-provisioned for programmability and efficiency. FPGAs do not have a software story, nor do they offer a clear path forward for edge deployments.

On the other hand, some devices are migrating toward GPUs and even more specialized accelerators, which often promise to make an application faster and more efficient. Both require working with new languages and APIs, and as soon as an application does not fit the paradigm, the benefits begin to degrade.

Highly specialized accelerators are also a risky choice, leaving developers with the question: “Will the program that I care about today be the one I care about tomorrow?” If not, designers must discard the accelerator entirely and completely re-design their application around new hardware. On top of this, an accelerator that supports only a narrow strip of an application’s underlying functions (e.g., convolutional neural networks) leaves the remainder of the application unaided and inefficient. Specialization presents a foundational risk to building robust and adaptable edge computing applications.
By shifting to highly energy-efficient, yet general-purpose processor architectures, we can avoid the overheads of von Neumann processors by spatially mapping a computation’s instructions across an array of hardware resources. If spatial dataflow architecture is incorporated, the result of one operation can be directly routed to the input of another operation, according to program dataflow without accessing any intermediate memory.
Additionally, spatial mapping can also minimize the costly data movement in a chip, as it eliminates the price of instruction supply and dataflow. The key to this generality is the co-design of a compiler and software stack to support developers with highly efficient dataflow hardware. The result is a new category of general-purpose processors that are programmable using traditional software, which avoids over-specialization or complicated language while providing orders of magnitude better energy efficiency than leading CPUs.
Especially as edge computing solutions become more integrated with multi-sensor systems, AI, and a broad array of computational demands, the industry needs vastly more programmable energy-efficient processors to alleviate energy constraints across five core industries: smart cities, agriculture, energy and gas, space and defense, and health-tech and wearables.

(Source: Efficient)

Smart cities and public sector​

Industrial edge devices are used to optimize traffic flow, monitor the health and condition of infrastructure, such as bridges, roads, and buildings, and improve public services in smart cities.
However, energy constraints can limit the deployment, density, and coverage of these devices, especially when paired with the cost and effort required to regularly and manually deploy, monitor, and replace batteries. More energy-efficient sensors would reduce the frequency of battery changes, eliminate the need for wired power connections in smart cities, and could be leveraged for continuous infrastructure and public space monitoring, traffic management, pest detection, waste management, smart lighting, and more.

Agriculture​

Similar devices are used extensively in agriculture for precision farming, monitoring crop health, agricultural fleet management, and managing resources like water and fertilizer. However, deploying these devices over geographically distributed areas with minimal power sources is also challenging due to the need for frequent battery replacements or recharging.
By eliminating the need for battery-related maintenance, farmers can significantly expand their sensor networks for enhanced monitoring and management of crops and operations. This shift would enable more efficient water and fertilizer deployment, leading to improved harvesting practices and pest mitigation, ultimately boosting crop yields and operations.

Energy and gas​

Edge devices are also used for real-time monitoring, maintenance, and control of crucial pipelines or power systems, enabling predictive maintenance and reducing downtime. However, the energy constraints of these devices limit the scale of their deployment.
In critical infrastructure, where continuous smart monitoring is a requirement, the operational cost of battery maintenance makes large-scale deployments infeasible. By advancing energy-efficient computing in these sensing devices, widespread sensor installations can be enabled—even in remote areas where renewable power sources like wind and solar are prevalent. This not only reduces maintenance time and outages, but also improves public safety, drives down route-based maintenance costs, and fosters more sustainable operations.

Space and defense​

In the space industry, edge devices face unique challenges related to energy usage constraints. These devices operate in harsh environments with extreme temperatures, radiation, and vacuum conditions, which can affect their performance and efficiency. Additionally, space missions often rely on limited power sources, such as solar panels or batteries, restricting the energy available for these devices.
This limitation is critical as missions can last from months to years, requiring edge devices to operate efficiently without the possibility of recharging or replacing batteries. Communication constraints further complicate energy-saving strategies and optimization efforts, as remote management and updates are limited. Given the high cost of deployment and the limited resources for maintenance or repairs once in space, ensuring the reliability and energy efficiency of these devices is paramount. Ultra energy-efficient processors would open opportunities for increased device lifespans, improved reliability, and more complex on-device operations for data gathering, communications, monitoring, and more.

Health-tech and wearables​

Most wearables like smartwatches or smart rings are limited to utilizing small batteries to keep the device lightweight and compact. This inherently limits the amount of energy available for the continuous processing, data transmission and communication tasks these devices are used for. A more energy-efficient processor for wearable devices would not only allow users to go longer in between charges, but greatly improve performance while consuming vastly less energy for the same or even more complex on-device tasks than what is currently on the market.
Today, devices spend a majority of their energy channeling data back to a nearby smartphone, offloading AI functionality to the phone and squandering energy on communication. However, new, more energy efficient computer architectures make it possible to perform sophisticated signal processing, analytics, machine learning and even generative AI functionality directly on even the tiniest devices.
Efficient computing locally uses vastly less energy and enables more sophisticated processing for more data collected by the device. Devices will spend the “dividends” of energy efficiency by adding more functionality to smart wearables. This will augment situational awareness, provide real-time translation, and interpret environmental and bio-sensory data to better understand behavioral and lifestyle factors surrounding health and wellness.
As the world continues to shift towards AI-specialized hardware and processors, older or less general-purpose devices are rapidly becoming obsolete, requiring more frequent replacements. This ongoing cycle of hardware replacement causes enormous production costs in both energy and carbon emissions, straining resources and exacerbating environmental degradation.
As more processing, analytics and AI find their way into sensor-enabled devices deployed to the extreme edge, the energy cost of computing becomes a more urgent, existential concern for these critically important application use cases. Addressing this challenge is vital not only for the efficiency and longevity of these devices but also for the sustainability of their deployment in our rapidly evolving technological landscape.

https://www.eetimes.com/energy-is-everything-for-edge-computing/