MDhere
Top 20
Are feeling the hour of power in your one min volume chart again reaching $1 to wipe out the board in the hour of power ?
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Are feeling the hour of power in your one min volume chart again reaching $1 to wipe out the board in the hour of power ?
Shadow59
Regular
I would like that
Wags
Regular
toasty
Regular
Maybe that's because we're not a chip maker??? Our IP could concievably be in all those company's products..........The good news is that the neuromorphic chip market is expected to reach USD 44.22 Billion by 2031.
The bad news is that this particular report doesn't mention us as a key player.
Neuromorphic Computing Chip Market Size, Share and Forecast By Key Players-IBM, Intel, Samsung Electronics, Qualcomm, Gyrfalcon
07-17-2024 06:33 PM CET | Advertising, Media Consulting, Marketing Research
Press release from: Market Research Intellect
Neuromorphic Computing Chip Market
𝐔𝐒𝐀, 𝐍𝐞𝐰 𝐉𝐞𝐫𝐬𝐞𝐲- According to the MRI Team's Market Research Intellect, the global Neuromorphic Computing Chip market is anticipated to grow at a compound annual growth rate (CAGR) of 16.89% between 2024 and 2031. The market is expected to grow to USD 14.84 Billion by 2024. The valuation is expected to reach USD 44.22 Billion by 2031.
The Neuromorphic Computing Chip market is poised for substantial growth, fueled by the increasing demand for advanced computing technologies that mimic human brain functions. This technology's potential in enhancing artificial intelligence (AI) applications and improving machine learning (ML) efficiency drives its adoption across various sectors, including healthcare, automotive, and consumer electronics. The market's expansion is further supported by ongoing research and development efforts and significant investments from key industry players aiming to capitalize on the advantages of neuromorphic computing in developing smarter, more efficient systems.
Several factors drive the Neuromorphic Computing Chip market's growth. The surging demand for AI and ML technologies that require advanced processing capabilities is a primary driver. These chips' ability to perform complex tasks with low power consumption makes them ideal for various applications, from autonomous vehicles to medical diagnostics. Additionally, increased investments in research and development by tech giants and startups alike are propelling innovation in this field. The growing need for real-time data processing and the development of smart devices further amplify the demand for neuromorphic computing chips, positioning them as a critical component in the future of computing technology.
𝐑𝐞𝐪𝐮𝐞𝐬𝐭 𝐏𝐃𝐅 𝐒𝐚𝐦𝐩𝐥𝐞 𝐂𝐨𝐩𝐲 𝐨𝐟 𝐑𝐞𝐩𝐨𝐫𝐭: (𝐈𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐅𝐮𝐥𝐥 𝐓𝐎𝐂, 𝐋𝐢𝐬𝐭 𝐨𝐟 𝐓𝐚𝐛𝐥𝐞𝐬 & 𝐅𝐢𝐠𝐮𝐫𝐞𝐬, 𝐂𝐡𝐚𝐫𝐭) @ https://www.marketresearchintellect...?rid=1065552&utm_source=OpenPr&utm_medium=026
𝐊𝐞𝐲 𝐃𝐫𝐢𝐯𝐞𝐫𝐬:
𝐈𝐧𝐜𝐫𝐞𝐚𝐬𝐢𝐧𝐠 𝐔𝐫𝐛𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧:The rising urban population is propelling the demand for Neuromorphic Computing Chip, especially in urban centers with a heightened need for specific aspects provided by Neuromorphic Computing Chip, driving the market growth.
𝐃𝐞𝐦𝐚𝐧𝐝 𝐟𝐨𝐫 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: Growing environmental concerns and stringent regulations are fostering a shift towards sustainable alternatives, boosting the demand for eco-friendly Neuromorphic Computing Chip products and services.
𝐌𝐞𝐫𝐠𝐞𝐫𝐬 𝐚𝐧𝐝 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧𝐬
𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐂𝐨𝐧𝐬𝐨𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: The Neuromorphic Computing Chip market is witnessing a wave of mergers and acquisitions as companies seek to consolidate their market positions, expand their product portfolios, and leverage synergies to drive growth and competitiveness.
𝐀𝐜𝐜𝐞𝐬𝐬 𝐭𝐨 𝐍𝐞𝐰 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬: Acquisitions provide companies with access to new technologies, intellectual property, and talent, enabling them to innovate faster and stay ahead of market trends.
𝐆𝐞𝐭 𝐚 𝐃𝐢𝐬𝐜𝐨𝐮𝐧𝐭 𝐎𝐧 𝐓𝐡𝐞 𝐏𝐮𝐫𝐜𝐡𝐚𝐬𝐞 𝐎𝐟 𝐓𝐡𝐢𝐬 𝐑𝐞𝐩𝐨𝐫𝐭 @ https://www.marketresearchintellect...?rid=1065552&utm_source=OpenPr&utm_medium=026
𝐓𝐡𝐞 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐊𝐞𝐲 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐬 𝐀𝐫𝐞 𝐂𝐨𝐯𝐞𝐫𝐞𝐝 𝐢𝐧 𝐎𝐮𝐫 𝐑𝐞𝐩𝐨𝐫𝐭
𝐁𝐲 𝐓𝐲𝐩𝐞
12nm
28nm
Others
𝐁𝐲 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧
Artificial Intelligence
Medical Equipment
Robot
Communications Industry
Other
𝐌𝐚𝐣𝐨𝐫 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 in Neuromorphic Computing Chip Market are:
IBM,Intel,Samsung Electronics,Qualcomm,Gyrfalcon,Eta Compute,Westwell,Lynxi,DeepcreatIC,SynSense
Neuromorphic Computing Chip Market Size, Share and Forecast By Key Players-IBM, Intel, Samsung Electronics, Qualcomm, Gyrfalcon
According to the MRI Team s Market Research Intellect the global Neuromorphic Computing Chip market is anticipated to grow at a compound annual growth rate CAGR of 16 89 between 2024 and 2031 The market is expected to grow to ...www.openpr.com
mrgds
Regular
Pom down under
Top 20
Great idea, I might join you.
Bravo
If ARM was an arm, BRN would be its biceps💪!
View attachment 66708
View attachment 66701
APPLICATIONS
New satellite to show how AI advances Earth observation
02/07/20242542 VIEWS14 LIKES
ESA / Applications / Observing the Earth / Φsat-2
Artificial intelligence technologies have achieved remarkable successes and continue to show their value as backbones in scientific research and real-world applications.
ESA’s new Φsat-2 mission, launching in the coming weeks, will push the boundaries of AI for Earth observation – demonstrating the transformative potential of AI for space technology.
Earth observation has, for decades, provided a rich stream of actionable data for scientists, businesses and policymakers. Thanks to new satellites and advanced sensors, the scale and quality of available Earth observation data have risen exponentially in the past decade.
The integration of AI has significantly enhanced Earth observation. AI capabilities allow for more data to be processed quickly and accurately, helping to enable to transform vast amounts of raw data into actionable insights.
As part of an initiative to promote the development and implementation of innovative technologies onboard Earth observation missions, ESA launched Ф-sat-1 in 2020. It was ESA’s first experiment to demonstrate how artificial intelligence can be used for Earth observation and paved the way for its successor: Φsat-2.
Φsat-2 integrated
Φsat-2 is a dedicated AI mission which will fully explore the benefits and capabilities of utilising extended onboard processing and further demonstrate the benefits of using AI for innovative Earth observation.
Measuring just 22 x 10 x 33 cm, ESA’s Φsat-2 satellite is equipped with a multispectral camera and powerful AI computer that analyses and processes imagery onboard – promising to deliver smarter and more efficient ways of monitoring our planet.
With six AI applications running onboard, the satellite is designed to turn images into maps, detect clouds in the images, classify them and provide insight into cloud distribution, detect and classify vessels, compress images on board and reconstruct them in the ground reducing the download time, spot anomalies in marine ecosystems and detect wildfires.
ESA’s Φsat-2 Technical Officer Nicola Melega, commented, “Φsat-2 will unlock a new era of real-time insights from space and will allow for custom AI apps to be easily developed, installed, and operated on the satellite even while in orbit. This adaptability maximises the satellite's value for scientists, businesses and governments.”
The Φsat-2 mission is a collaborative effort between ESA and Open Cosmos who serves as the prime contractor, supported by an industrial consortium including Ubotica, GGI, CEiiA, GEO-K, KP-Labs, and SIMERA.
Φsat-2 integration into the Exolaunch deployer
Access the video
Φsat-2, which shares its ride into orbit with ESA’s Arctic Weather Satellite, is scheduled to liftoff in July 2024 on a SpaceX Falcon 9 from the Vandenberg Air Force Base, California, in the US.
Φsat-2 carries a multispectral instrument that images Earth in seven different bands and, through its AI applications, is capable of many things that can provide actionable information on the ground, including:
Cloud detection
Unlike traditional satellites that downlink all captured images, including those obscured by clouds, Φsat-2 processes these images directly in orbit, ensuring that only clear, usable images are sent back to Earth.
Developed by KP Labs, this application can also classify clouds and provide insights into cloud distribution. This gives users more flexibility when it is time to decide whether an image is usable or not.
Street map generation
The Sat2Map application, developed by CGI, converts satellite imagery into street maps. This capability is particularly beneficial for emergency response teams, enabling them to identify accessible roads during disasters such as floods or earthquakes.
When the satellite orbits over the affected area and acquires images, the images are passed to the onboard processer that will identify streets and generate a corresponding map.
Initially, this application will be demonstrated over Southeast Asia, showcasing its potential to aid in crisis management.
Maritime vessel detection
The maritime vessel detection application, developed by CEiiA, utilises machine learning techniques to automatically detect and classify vessels in specified regions, facilitating the monitoring of activities like illegal fishing. This application underscores the satellite’s role in supporting maritime security and environmental conservation efforts.
Training Φsat-2’s autonomous vessel awareness application
On-board image compression and reconstruction
Developed by GEO-K, this application is responsible for compressing images on board. By significantly reducing file sizes, this application increases the volume and speed of data downloads. After being downlinked to the ground, the images are reconstructed using a dedicated decoder. The first demonstrations of this technology will occur over Europe, focusing on the detection of buildings.
Φsat-2’s capabilities have been further expanded with the incorporation of two additional AI applications that will be uploaded once the satellite is in orbit.
These AI applications were the winning entries in the OrbitalAI challenge organized by ESA’s Φ-lab and was designed to give companies the change to pioneer in-orbit Earth observation data processing. The winning applications are:
Marine anomaly detection
Developed by IRT Saint Exupery Technical Research, this application uses machine learning algorithm to spot anomalies in marine ecosystems – identifying threats to the marine ecosystem such as oil spills, harmful algae blooms and heavy sediment discharges in real-time.
Wildfire detection
The wildfire detection system, developed by Thales Alenia Space, uses machine learning to provide critical real-time information to response teams. The tool provides a classification report that helps firefighters locate wildfires, track fire spread and identify potential hazards.
PhiFireAI classification
New satellite to show how AI advances Earth observation
Artificial intelligence technologies have achieved remarkable successes and continue to show their value as backbones in scientific research and real-world applications.ESA’s new Φsat-2 mission, launching in the coming weeks, will push the boundaries of AI for Earth observation – demonstrating...www.esa.int
To the naysayers, who always seem to proclaim that we would never be able to compete with the likes of industry Goliath's such as Intel.
Well, from the latest announcement, I would say it looks like BrainChip is well and truly being chosen IN SPACE, which is pretty cool or pretty rad-hard depending on your own interpretation of things.
This is what Mark Davies, CEO of Intel had to say in this article from March 2023.
Last edited:
wilzy123
Founding Member
Do you know how the cordless drill first came about?To the naysayers, who always seem to proclaim that we would never be able to compete with the likes of industry Goliath's such as Intel.
Well, from the latest announcement, I would say it looks like BrainChip is well and truly being chosen IN SPACE, which is pretty cool or pretty rad-hard depending on your own interpretation of things.
This is what Mark Davies, CEO of Intel had to say in this article from March 2023.
View attachment 66726
'Well aware that outer space didn't come with electrical sockets, NASA joined with Black and Decker, which was already working on cordless technology in the 1950s [source: NASA 360].'
Now look at how much is cordless in the world today.
Just saying.
https://www.nasa.gov/wp-content/uploads/static/60counting/NASA: 60 Years & Counting - Technology
https://www.nasa.gov/wp-content/uploads/static/60counting/We celebrate NASA’s first 60 years of achievement
www.nasa.gov
Just saying.
Food4 thought
Regular
Something tell me I can’t wait that longhttps://www.nasa.gov/wp-content/uploads/static/60counting/NASA: 60 Years & Counting - Technology
https://www.nasa.gov/wp-content/uploads/static/60counting/We celebrate NASA’s first 60 years of achievementwww.nasa.gov
Just saying.
Another 60 years
Oh dam!
That’s just to long to wait
Dallas
Regular
Tom's Hardware on LinkedIn: Tenstorrent's RISC-V-based Wormhole AI accelerators are available for…
Tenstorrent's RISC-V-based Wormhole AI accelerators are available for pre-order today — pre-built workstations start at $12,000 https://trib.al/M6aowXh
www.linkedin.com
Dallas
Regular
Tenstorrent COO Keith Witek and BrainChip Discuss Technology Trends and the growth of AI on their ‘This is Our Mission’ Podcast
Join us as we delve into deep conversations on the 'This Is Our Mission' podcast, exploring the forefront of AI advancements.
brainchip.com
Fullmoonfever
Top 20
I see we got noticed in a current global waste management market research report. A slice of that mkt would be welcome.
Snipped some bits.
Snipped some bits.
Smart Bins for Waste Management Market Size, Outlook - 2034
The smart bins for waste management market was valued at US$ 75.3 Mn in 2023; It is estimated to advance at a CAGR of 13.1% from 2024 to 2034 and reach US$ 292.4 Mn by the end of 2034
www.transparencymarketresearch.com
Fullmoonfever
Top 20
Wonder if this is why Dr Machado is excited to get his hands on AkidaLooks like Pedro is enjoying his new toy so far
Search • Threads
www.threads.net
View attachment 65618
AQUABRAIN | Nottingham Trent University
Developing AI tools for marine biologists, aquaculture farmers, and ecologists, to conduct real-time monitoring and decision-making related to underwater life, pollutant agents, and various species.
www.ntu.ac.uk
The AQUABRAIN impact case focused on Behavioural Analysis for AI-assisted Decision-Making (BEHAVIOR-AI) is underpinned by research focusing on the utilisation of Intelligent Edge devices, which operate independently of the internet using AI and Machine Learning algorithms. This research has generated valuable insights and findings by integrating behavioural analysis with AI algorithms, with applications in healthcare [R1, R5], agriculture/aquaculture [R1, R2, R3, R4] and environmental monitoring [R1, R2, R3, R4]. Personalised temperature regulation using Intelligent Edge devices has been identified as a significant factor in improving well-being and preventing heat-related illnesses.
Dr Pedro Machado and Dr Isibor Kennedy Ihianle have contributed their expertise to the research, with their respective interests in neuromorphic engineering, edge computing, and pervasive computing. The research is interdisciplinary, emphasising sustainable practices, and aligned with the Sustainable Development Goals. The contextual aspects include advancements in edge computing, interdisciplinary collaboration, and the aim to enhance health and quality of life while addressing climate action and the preservation of aquatic ecosystems.
Tothemoon24
Top 20
The Audio Revolution at the Edge
In today’s interconnected world, clear and noise-free audio has become more crucial than ever. From wireless earbuds to smart home devices and enterprise communication systems, the demand for high-quality audio processing continues to grow. At BrainChip, we’re excited to introduce our groundbreaking Audio Denoising solution, powered by our innovative Temporal Event Neural Network (TENNs); a platform of use case specific algorithms coupled with our Akida™ event-based semiconductor IP that improve performance and power efficiency. TENNs represents a significant leap forward in audio processing, offering unparalleled efficiency and performance for edge computing applications.
The Challenge of Audio Denoising
Audio denoising, the process of removing unwanted noise from audio signals, has long been a complex challenge in signal processing. Traditional methods often struggle to balance noise reduction while preserving the original signal’s quality. As well, today’s audio pre-processing approaches are computationally and energy inefficient.
Enter TENNs: A New Paradigm in Audio Processing
BrainChip’s Audio Denoising solution leverages the power of TENNs, a revolutionary approach to neural network architecture that excels in processing sequential and continuous data streams. By combining the principles of state space models and generalized convolution kernels, TENNs offers a highly efficient alternative to traditional transformer models, making it ideal for edge computing audio applications.
Key Features and Benefits
Scalable TENNs Models: Our medium-sized model achieves an impressive Perceptual Evaluation of
Speech Quality (PESQ) score of 3.36 with just 590,000 parameters. This demonstrates TENN’s ability to
deliver excellent noise reduction while maintaining audio quality. The TENN architecture allows for easy
scaling to smaller or larger models, adapting to specific customer needs without compromising
performance.
Unmatched Efficiency: TENN models require fewer parameters and multiply-accumulate operations
(MACs) per sample compared to equivalent CNN-based models. In fact, our Audio Denoising solution
uses 12 times fewer MACs and nearly 3 times fewer parameters than state-of-the-art networks, while
providing comparable performance. This translates to significantly lower power consumption and
reduced area requirements when designing System-on-Chip (SoC) solutions.
Hardware IP Integration: Our product includes Hardware IP, enabling companies to seamlessly
incorporate BrainChip AI acceleration into their SoC designs. This integration ensures optimal
performance and efficiency for audio denoising tasks, making it ideal for a wide range of edge devices.
Versatile Applications: From in-ear wireless devices to fixed audio equipment and VoIP systems, our
audio denoising solution caters to a broad spectrum of applications. It can be implemented as a
standalone feature or integrated into a pipeline feeding speech recognition or keyword spotting
systems, which BrainChip is also developing.
Customizable Solutions: With our TENNs license, customers can fine-tune models to their specific
requirements, ensuring the best possible performance for their unique audio environments and use
cases.
Real-World Applications and Performance
Our Audio Denoising solution powered by TENNs has demonstrated impressive results across various
applications:
Enhanced Speech Clarity: Improve the quality of voice calls and audio recordings by removing
background noise and focusing on the speaker’s voice. This is particularly valuable for mobile
devices and hearing aids.
Improved VoIP Communication: Elevate the experience of video conferencing and online
meetings with clearer, noise-free audio, essential in today’s remote work environment.
Smart Home Devices: Enhance the accuracy of voice-controlled smart home systems by
providing cleaner audio input, improving user experience and device functionality.
Industrial Monitoring: Improve the quality of audio data in industrial settings, enabling more
accurate analysis and predictive maintenance.
Medical Devices: Vital sign estimation, allowing for more accurate and power efficient
monitoring of human health.
When compared to state-of-the-art networks for speech enhancement through denoising, our TENNs-
based solution achieved:
– Comparable PESQ scores (3.36 for the medium model)
– Approximately 12 times fewer MACs (Multiply-Accumulate operations)
– Nearly 3 times fewer parameters
These results highlight the exceptional efficiency of our TENNs Audio Denoising solution, making it ideal
for edge computing applications where power consumption and computational resources are limited.
Also note that the ability to execute on raw data eliminates the need for expensive pre-processing.
Implementation and Future Directions
The implementation of our Audio Denoising offering within BrainChip’s hardware, specifically in the
Akida 2.0 IP showcases a significant advancement in hardware-accelerated AI for audio processing.
Akida 2.0’s architecture is designed to fully exploit TENN’s capabilities, featuring a mesh network of
nodes, each equipped with an event-based TENN processing unit.
Looking ahead, we plan to continue refining our Audio Denoising capabilities, focusing on:
Enhancing activation sparsity to further improve efficiency.
Exploring more of the polynomial space to increase model flexibility.
Developing integrated solutions that combine audio denoising with speech recognition and keyword spotting.
A New Era of Crystal-Clear Audio
BrainChip’s TENNs-powered Audio Denoising solution marks a significant milestone in the evolution of audio processing technology. By addressing key challenges related to power consumption and computational efficiency, we’re paving the way for a new generation of edge devices capable of delivering crystal-clear audio in any environment. Our Audio Denoising product is just the beginning of what’s possible with TENNs technology, and we’re excited to see how our partners and customers will leverage this powerful algorithm platform to create the next generation of audio-enabled devices. To learn more about how BrainChip’s Audio Denoising solution can elevate your products, contact us for a demonstration or to discuss your specific audio processing needs.
Similar threads
