BRN Discussion Ongoing
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GDJR69
Regular
Well at least we are getting better known, If you search 'neuromorphic engineering' on Wikipedia it relevantly says:
'Brainchip announced in October 2021 that it was taking orders for its Akida AI Processor Development Kits[36] and in January 2022 that it was taking orders for its Akida AI Processor PCIe boards,[37] making it the world's first commercially available neuromorphic processor.'
'Brainchip announced in October 2021 that it was taking orders for its Akida AI Processor Development Kits[36] and in January 2022 that it was taking orders for its Akida AI Processor PCIe boards,[37] making it the world's first commercially available neuromorphic processor.'
Learning
Learning to the Top 🕵♂️
Little Brainchip is among the big boy
.
"Neuromorphic Computing Market size is anticipated to cross USD 2626.4 Billion by the end of 2035"
Neuromorphic Computing Market Share, Growth, Regional Analysis, Prominent Players and Forecast 2035
PRESS RELEASE
Published June 27, 2023
Comserve Online
New York, United States, Tue, 27 Jun 2023 10:55:46 / Comserve Inc. / -- Neuromorphic Computing Market size is anticipated to cross USD 2626.4 Billion by the end of 2035, growing at a CAGR of 92% during the forecast period i.e., 2023-2035.
Research Nester’s recent market research analysis on “Neuromorphic Computing Market: Global Demand Analysis & Opportunity Outlook 2035” delivers a detailed competitors analysis and a detailed overview of the global neuromorphic computing market in terms of market segmentation by offering, deployment, application, end users, and by region.
Growing Demand for Automated Vehicles Made Using Artificial Intelligence and Machine Learning to Promote Global Market Share of Neuromorphic Computing
The global neuromorphic computing market is estimated to grow majorly on account of the increased installation of self-driving technology in automobiles and growing research and development for level 5 automation in automobiles. Also, the rising use of neuromorphic chips in the production of miniature and small electronic devices to achieve customer compliance is projected to fuel market growth in the coming years. The enhanced application of neuromorphic computing in the media and entertainment sectors to analyze the buying patterns of consumers is driving the market growth.
The market expansion is also attributed to the increasing use of voice recognition devices and speech assistants in smart devices including smartphones, computers, and laptops. The increasing application of neuromorphic chips in the cameras such as drones is estimated to augment the market growth as they sense the activities and move so fast resembling human brains. The enhancement in the market size is attributed to the utilization of artificial intelligence and machine learning technology in industrial robots to reduce labor costs and the risk of accidents during loading and unloading works in heavy machinery manufacturing sectors.
Some of the major growth factors and challenges that are associated with the growth of the global neuromorphic computing market are:
Growth Drivers:
Hike in the Use of Neuromorphic Technology in the Drones and Chatterbots
Growing Demand for Biological Like Robotics and Cognitive Robots with Intelligence of the Human Brain
Challenges:
The limited opportunities for research and development of neuromorphic computing technology and slow deployment chances, along with the rise in the complexity of algorithms ad backed operations are some of the major factors anticipated to hamper the global market size of neuromorphic computing. There is an increasing need for constant advancement in hardware and software systems used in neuromorphic computing to stay updated but the lack of funds, skilled professionals, and time-consuming process is hindering the growth of the market.
By end-users, the global neuromorphic computing market is segmented into aerospace, consumer electronics, automotive, IT & telecom, military & defense, industrial, and medical. The medical segment is to garner a noteworthy share of the market by the end of 2035. There has been an escalating demand for wearables across the world that detect the change in heart rate, cholesterol, and blood pressure of the individual. In addition, an increasing application of neuromorphic computing in medical diagnostics to achieve accurate and speedy results is poised to rise the growth opportunities of the market segment. The progression of the market segment is attributed to the wide variety of applications so computing in the medical field such as image recognition for the early detection of diseases using artificial neural networks that mimic the human brain in processing information.
By region, the Asia Pacific neuromorphic computing market is to generate substantial revenue by the end of 2035. This growth is anticipated by increasing applications of advanced technologies such as artificial intelligence, data analytics, the Internet of Things, and others in modern software. The implementation of these technologies in electronic devices to develop progressed cognitive products that work on their own with the use of neuromorphic chips and neuromorphic computing is driving the market growth in the region. The growing IT sector in the region and the high usage of the internet, as well as other smart electronic devices and appliances, are propelling the market size.
This report also provides the existing competitive scenario of some of the key players of the global neuromorphic computing market which includes company profiling of IBM Corporation, Intel Corporation, Brainchip Holdings Limited, Qualcomm Technologies, HP Enterprise, HRL Laboratories LLC, Flow Neuroscience AB, Innatera Nanosystems B.V., Aspinity, Inc., Samsung Electronics Limited, and others.
Research Nester is a leading service provider for strategic market research and consulting. We aim to provide unbiased, unparalleled market insights and industry analysis to help industries, conglomerates and executives to take wise decisions for their future marketing strategy, expansion and investment etc. We believe every business can expand to its new horizon, provided a right guidance at a right time is available through strategic minds. Our out of box thinking helps our clients to take wise decision in order to avoid future uncertainties.
For more information, please contact:
AJ Daniel
Research Nester
Email: info@researchnester.com
Tel: +1 646 586 9123
Read more: https://www.digitaljournal.com/pr/n...Pa_UDqf2qXH-QWtu0hABPDi3J26AE3k#ixzz85vDRtOPR
Learning 🏖
."Neuromorphic Computing Market size is anticipated to cross USD 2626.4 Billion by the end of 2035"
Neuromorphic Computing Market Share, Growth, Regional Analysis, Prominent Players and Forecast 2035
PRESS RELEASE
Published June 27, 2023
Comserve Online
New York, United States, Tue, 27 Jun 2023 10:55:46 / Comserve Inc. / -- Neuromorphic Computing Market size is anticipated to cross USD 2626.4 Billion by the end of 2035, growing at a CAGR of 92% during the forecast period i.e., 2023-2035.
Research Nester’s recent market research analysis on “Neuromorphic Computing Market: Global Demand Analysis & Opportunity Outlook 2035” delivers a detailed competitors analysis and a detailed overview of the global neuromorphic computing market in terms of market segmentation by offering, deployment, application, end users, and by region.
Growing Demand for Automated Vehicles Made Using Artificial Intelligence and Machine Learning to Promote Global Market Share of Neuromorphic Computing
The global neuromorphic computing market is estimated to grow majorly on account of the increased installation of self-driving technology in automobiles and growing research and development for level 5 automation in automobiles. Also, the rising use of neuromorphic chips in the production of miniature and small electronic devices to achieve customer compliance is projected to fuel market growth in the coming years. The enhanced application of neuromorphic computing in the media and entertainment sectors to analyze the buying patterns of consumers is driving the market growth.
The market expansion is also attributed to the increasing use of voice recognition devices and speech assistants in smart devices including smartphones, computers, and laptops. The increasing application of neuromorphic chips in the cameras such as drones is estimated to augment the market growth as they sense the activities and move so fast resembling human brains. The enhancement in the market size is attributed to the utilization of artificial intelligence and machine learning technology in industrial robots to reduce labor costs and the risk of accidents during loading and unloading works in heavy machinery manufacturing sectors.
Some of the major growth factors and challenges that are associated with the growth of the global neuromorphic computing market are:
Growth Drivers:
Hike in the Use of Neuromorphic Technology in the Drones and Chatterbots
Growing Demand for Biological Like Robotics and Cognitive Robots with Intelligence of the Human Brain
Challenges:
The limited opportunities for research and development of neuromorphic computing technology and slow deployment chances, along with the rise in the complexity of algorithms ad backed operations are some of the major factors anticipated to hamper the global market size of neuromorphic computing. There is an increasing need for constant advancement in hardware and software systems used in neuromorphic computing to stay updated but the lack of funds, skilled professionals, and time-consuming process is hindering the growth of the market.
By end-users, the global neuromorphic computing market is segmented into aerospace, consumer electronics, automotive, IT & telecom, military & defense, industrial, and medical. The medical segment is to garner a noteworthy share of the market by the end of 2035. There has been an escalating demand for wearables across the world that detect the change in heart rate, cholesterol, and blood pressure of the individual. In addition, an increasing application of neuromorphic computing in medical diagnostics to achieve accurate and speedy results is poised to rise the growth opportunities of the market segment. The progression of the market segment is attributed to the wide variety of applications so computing in the medical field such as image recognition for the early detection of diseases using artificial neural networks that mimic the human brain in processing information.
By region, the Asia Pacific neuromorphic computing market is to generate substantial revenue by the end of 2035. This growth is anticipated by increasing applications of advanced technologies such as artificial intelligence, data analytics, the Internet of Things, and others in modern software. The implementation of these technologies in electronic devices to develop progressed cognitive products that work on their own with the use of neuromorphic chips and neuromorphic computing is driving the market growth in the region. The growing IT sector in the region and the high usage of the internet, as well as other smart electronic devices and appliances, are propelling the market size.
This report also provides the existing competitive scenario of some of the key players of the global neuromorphic computing market which includes company profiling of IBM Corporation, Intel Corporation, Brainchip Holdings Limited, Qualcomm Technologies, HP Enterprise, HRL Laboratories LLC, Flow Neuroscience AB, Innatera Nanosystems B.V., Aspinity, Inc., Samsung Electronics Limited, and others.
Research Nester is a leading service provider for strategic market research and consulting. We aim to provide unbiased, unparalleled market insights and industry analysis to help industries, conglomerates and executives to take wise decisions for their future marketing strategy, expansion and investment etc. We believe every business can expand to its new horizon, provided a right guidance at a right time is available through strategic minds. Our out of box thinking helps our clients to take wise decision in order to avoid future uncertainties.
For more information, please contact:
AJ Daniel
Research Nester
Email: info@researchnester.com
Tel: +1 646 586 9123
Read more: https://www.digitaljournal.com/pr/n...Pa_UDqf2qXH-QWtu0hABPDi3J26AE3k#ixzz85vDRtOPR
Learning 🏖
Last edited:
JDelekto
Regular
Being listed among several tech giants is an excellent achievement.Little Brainchip is among the big boy
"Neuromorphic Computing Market size is anticipated to cross USD 2626.4 Billion by the end of 2035"
Neuromorphic Computing Market Share, Growth, Regional Analysis, Prominent Players and Forecast 2035
PRESS RELEASE
Published June 27, 2023
Comserve Online
New York, United States, Tue, 27 Jun 2023 10:55:46 / Comserve Inc. / -- Neuromorphic Computing Market size is anticipated to cross USD 2626.4 Billion by the end of 2035, growing at a CAGR of 92% during the forecast period i.e., 2023-2035.
Research Nester’s recent market research analysis on “Neuromorphic Computing Market: Global Demand Analysis & Opportunity Outlook 2035” delivers a detailed competitors analysis and a detailed overview of the global neuromorphic computing market in terms of market segmentation by offering, deployment, application, end users, and by region.
Growing Demand for Automated Vehicles Made Using Artificial Intelligence and Machine Learning to Promote Global Market Share of Neuromorphic Computing
The global neuromorphic computing market is estimated to grow majorly on account of the increased installation of self-driving technology in automobiles and growing research and development for level 5 automation in automobiles. Also, the rising use of neuromorphic chips in the production of miniature and small electronic devices to achieve customer compliance is projected to fuel market growth in the coming years. The enhanced application of neuromorphic computing in the media and entertainment sectors to analyze the buying patterns of consumers is driving the market growth.
The market expansion is also attributed to the increasing use of voice recognition devices and speech assistants in smart devices including smartphones, computers, and laptops. The increasing application of neuromorphic chips in the cameras such as drones is estimated to augment the market growth as they sense the activities and move so fast resembling human brains. The enhancement in the market size is attributed to the utilization of artificial intelligence and machine learning technology in industrial robots to reduce labor costs and the risk of accidents during loading and unloading works in heavy machinery manufacturing sectors.
Some of the major growth factors and challenges that are associated with the growth of the global neuromorphic computing market are:
Growth Drivers:
Hike in the Use of Neuromorphic Technology in the Drones and Chatterbots
Growing Demand for Biological Like Robotics and Cognitive Robots with Intelligence of the Human Brain
Challenges:
The limited opportunities for research and development of neuromorphic computing technology and slow deployment chances, along with the rise in the complexity of algorithms ad backed operations are some of the major factors anticipated to hamper the global market size of neuromorphic computing. There is an increasing need for constant advancement in hardware and software systems used in neuromorphic computing to stay updated but the lack of funds, skilled professionals, and time-consuming process is hindering the growth of the market.
By end-users, the global neuromorphic computing market is segmented into aerospace, consumer electronics, automotive, IT & telecom, military & defense, industrial, and medical. The medical segment is to garner a noteworthy share of the market by the end of 2035. There has been an escalating demand for wearables across the world that detect the change in heart rate, cholesterol, and blood pressure of the individual. In addition, an increasing application of neuromorphic computing in medical diagnostics to achieve accurate and speedy results is poised to rise the growth opportunities of the market segment. The progression of the market segment is attributed to the wide variety of applications so computing in the medical field such as image recognition for the early detection of diseases using artificial neural networks that mimic the human brain in processing information.
By region, the Asia Pacific neuromorphic computing market is to generate substantial revenue by the end of 2035. This growth is anticipated by increasing applications of advanced technologies such as artificial intelligence, data analytics, the Internet of Things, and others in modern software. The implementation of these technologies in electronic devices to develop progressed cognitive products that work on their own with the use of neuromorphic chips and neuromorphic computing is driving the market growth in the region. The growing IT sector in the region and the high usage of the internet, as well as other smart electronic devices and appliances, are propelling the market size.
This report also provides the existing competitive scenario of some of the key players of the global neuromorphic computing market which includes company profiling of IBM Corporation, Intel Corporation, Brainchip Holdings Limited, Qualcomm Technologies, HP Enterprise, HRL Laboratories LLC, Flow Neuroscience AB, Innatera Nanosystems B.V., Aspinity, Inc., Samsung Electronics Limited, and others.
Research Nester is a leading service provider for strategic market research and consulting. We aim to provide unbiased, unparalleled market insights and industry analysis to help industries, conglomerates and executives to take wise decisions for their future marketing strategy, expansion and investment etc. We believe every business can expand to its new horizon, provided a right guidance at a right time is available through strategic minds. Our out of box thinking helps our clients to take wise decision in order to avoid future uncertainties.
For more information, please contact:
AJ Daniel
Research Nester
Email: info@researchnester.com
Tel: +1 646 586 9123
Read more: https://www.digitaljournal.com/pr/n...Pa_UDqf2qXH-QWtu0hABPDi3J26AE3k#ixzz85vDRtOPR
Learning 🏖
However, something about "Brainchip Holdings Limited" doesn't reflect the technological edge of one of the market leaders in its space.
Hopefully one day it can be changed to "BrainChip Innovations", "BrainChip Neuromorphic" or something more sci-fi.
Also, I wish they would fix the inconsistencies of "Brainchip" vs. "BrainChip". I see the latter used more often, but both of them are used in announcements on their Web site.
Learning
Learning to the Top 🕵♂️
Well Brainchip's Akida is the only commercial available digital Neuromorphic processor. (From the Web site: BrainChip’s first-to-market, digital neuromorphic processor IP, Akida™)
'Brainchip Neuromorphic' does have a ring to it.
Let associate the word Neuromorphic with the Brainchip brand. And let it be known, Brainchip have the first digital neuromorphic processor available.
Learning 🏖
Fullmoonfever
Top 20
From Hitachi's R&D pages from Feb 23.
They appear, amongst other things, to be investigating neuromorphic...currently research with Loihi 2 at this point but never know hey...maybe BRN best knock on the door
The human brain is known to consume a mere 20 W of power. By using neuromorphic algorithms and innovative devices to mimic its operation, the energy required to perform image recognition by techniques such as deep learning can be reduced more than 100-fold compared to a GPU. Hitachi has developed recognition algorithms suitable for use in video surveillance. Because this allows advanced recognition tasks such as identifying the attributes or actions of people to run on less than 1 W of power, it can be incorporated into security cameras, drones, or other edge devices to perform real-time, on-the-spot incident detection without having to send back video for processing on a server or the cloud.
In the future, Hitachi intends to help make people safer and more secure by investigating the use of this technology in real-time monitoring and surveillance solutions for public transportation and workplaces as well as other indoor and outdoor locations.
［24］Comparison of computing environments for deep learning and potential applications
They appear, amongst other things, to be investigating neuromorphic...currently research with Loihi 2 at this point but never know hey...maybe BRN best knock on the door
24. Neuromorphic Computing that Expands the Scope of Edge AI with Ultra-low Power Consumption
AI techniques such as image recognition are now finding applications in edge devices. However, if AI is to be widely adopted in the diverse environments at the network edge, power consumption needs to be as low as possible.The human brain is known to consume a mere 20 W of power. By using neuromorphic algorithms and innovative devices to mimic its operation, the energy required to perform image recognition by techniques such as deep learning can be reduced more than 100-fold compared to a GPU. Hitachi has developed recognition algorithms suitable for use in video surveillance. Because this allows advanced recognition tasks such as identifying the attributes or actions of people to run on less than 1 W of power, it can be incorporated into security cameras, drones, or other edge devices to perform real-time, on-the-spot incident detection without having to send back video for processing on a server or the cloud.
In the future, Hitachi intends to help make people safer and more secure by investigating the use of this technology in real-time monitoring and surveillance solutions for public transportation and workplaces as well as other indoor and outdoor locations.
［24］Comparison of computing environments for deep learning and potential applications
Dr E Brown
Regular
This is simple and pretty cool.
Sirod69
bavarian girl ;-)
Magnus ÖstbergMagnus Östberg• Chief Software Officer at Mercedes-Benz AG
39 Min.
Apparently, you are as excited as I am about our integration of #ChatGPT!
I was overwhelmed with questions about our beta programme. First, let’s cover some basics.Our software team integrated the ChatGPT-3.5 large language model via a #Microsoft API and the three-month beta is available in all 900,000 MBUX-equipped vehicles in the U.S. as of June 16, giving our tech-savvy U.S. customers an opportunity to test it. Now, let me answer some of your asked questions…
𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐰𝐨𝐫𝐤?
By integrating the ChatGPT large language model, the #MBUX Voice Assistant can interact more naturally and adaptively with our customers. When ChatGPT transmits an answer, it is also marked accordingly in our user interface.
𝐖𝐢𝐥𝐥 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐫𝐞𝐩𝐥𝐚𝐜𝐞 𝐭𝐡𝐞 𝐞𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐧𝐚𝐯𝐢𝐠𝐚𝐭𝐢𝐨𝐧?
When requesting directions, addresses and traffic data are verified by our MBUX software to ensure that they are correct.
𝐈 𝐰𝐨𝐮𝐥𝐝 𝐥𝐨𝐯𝐞 𝐭𝐨 𝐣𝐨𝐢𝐧 𝐭𝐡𝐞 𝐛𝐞𝐭𝐚 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐞, 𝐰𝐢𝐥𝐥 𝐢𝐭 𝐛𝐞 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐢𝐧 𝐄𝐮𝐫𝐨𝐩𝐞?
The beta programme is currently only available in the USA. However, all Mercedes-Benz owners will benefit since we are using the data collected to optimise our voice control in other markets and languages.
𝐃𝐨𝐞𝐬 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐬𝐞𝐚𝐫𝐜𝐡 𝐭𝐡𝐞 𝐥𝐚𝐭𝐞𝐬𝐭 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭 𝐜𝐨𝐧𝐭𝐞𝐧𝐭?
ChatGPT-3.5 provides access to a large language model trained on data available prior to 2021. The Mercedes-Benz Voice Assistant retains complete access to our current and authenticated content, including the latest navigation information. As ChatGPT continues to develop, our software will be able to provide the most up-to-date responses available through the Azure OpenAI service. Our MBUX software ensures that the results are correct.
𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐭𝐡𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐦𝐚𝐧𝐚𝐠𝐞 𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐨𝐫 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧?
Mercedes-Benz manages customer data, which is securely processed via our Mercedes-Benz Intelligent Cloud. Neither Microsoft nor ChatGPT parent company OpenAI can access Mercedes customer data. Only anonymized data is exchanged with Microsoft’s Azure OpenAI service.
𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐭𝐡𝐚𝐭 𝐞𝐧𝐬𝐮𝐫𝐞 𝐦𝐲 𝐝𝐚𝐭𝐚 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐩𝐫𝐨𝐭𝐞𝐜𝐭 𝐦𝐲 𝐩𝐫𝐢𝐯𝐚𝐜𝐲?
The security of Mercedes-Benz systems, customers, customer data and our vehicles are top priorities. We approach the integration of ChatGPT in alignment with our own responsible approach to information security principles. We guarantee data privacy, and this is an optional program that must be actively initiated by the customer. Our customers will always be informed about exactly what data we collect.
cosors
👀
"2023 Best of Sensors award winners announced at Sensors Converge
...
Automotive/Autonomous Technologies: Valeo | Valeo SCALA 3
..."
https://www.fierceelectronics.com/sensors/2023-best-sensors-award-winners-announced-sensors-converge
Frangipani
Regular
The potential for neuromorphic technology applications never ceases to amaze me…
Hands up, who has heard of Hermetia Illucens before?
No, it’s not a character from Harry Potter, however, the title (not content!) of another work of literature springs to mind regarding the following project’s collaborators: Lord(s) of the Flies.
Researchers from WSU’s International Centre for Neuromorphic Systems (ICNS) and the Department of Nutrition and Food Science have teamed up with researchers from Macquarie University’s School of Engineering as well as Industry Partner ARC Ento Tech, who are promoting an innovative way of processing MSW (municipal solid waste), partly with assistance of said Hermetia Illucens aka the black soldier fly. Actually, a huge army of them.
(https://www.arcentotechltd.com.au/bsf)
More details on the project itself and its budget/funding provided in the links below.
sensing, to enable high-speed counting of black soldier fly larvae (BSFL) to
optimise their breeding and production process. BSFLs are used to
process mixed solid waste and convert them into protein rich, high-value
food and feeds ingredient – a critical piece in achieving a circular economy.
ARC Ento Tech have developed an innovative process to treat waste over
the last two years, showing good traction in the industry. However, the
process relies on an optimised production of the flies. Currently the breeding
and production process is based on best practices, but there is a need to
quantify the fly production rate using smart sensors. Traditionally this has
been difficult to achieve due to their small size, yet the extremely high
number (100s of flies per minute). The high-speed counting enabled by
neuromorphic imaging, an expertise of Western Sydney University, will
deliver valuable insights to their processes.
sensing, to enable high-speed counting of black soldier fly larvae (BSFL) to
optimise their breeding and production process. BSFLs are used to
process mixed solid waste and convert them into protein rich, high-value
food and feeds ingredient – a critical piece in achieving a circular economy.
The key stages of the ARC Process™ use two component core technologies protected under the patent pending registration and form the heart of the system:
This consists of a ‘digesting circuit’ that provides ideal conditions for the Black Soldier Fly larvae to flourish and optimize nutrient recovery. The recovered nutrients are then re-formed and re-purposed into ‘new’ products that are channeled back into the consumption cycle.
All indigestible organics (such as paper, textile, wood, rubber, leather) and plastics left behind after this process move on to the RDR™ mechanical process which then converts them into another commercial product. This unique process makes for ARC’s total solution to MSW.
LEARN MORE ABOUT THE BSF
Hands up, who has heard of Hermetia Illucens before?
No, it’s not a character from Harry Potter, however, the title (not content!) of another work of literature springs to mind regarding the following project’s collaborators: Lord(s) of the Flies.
Researchers from WSU’s International Centre for Neuromorphic Systems (ICNS) and the Department of Nutrition and Food Science have teamed up with researchers from Macquarie University’s School of Engineering as well as Industry Partner ARC Ento Tech, who are promoting an innovative way of processing MSW (municipal solid waste), partly with assistance of said Hermetia Illucens aka the black soldier fly. Actually, a huge army of them.
(https://www.arcentotechltd.com.au/bsf)
More details on the project itself and its budget/funding provided in the links below.
High-speed counting of black soldier flies for optimised waste recovery
- Mukhopadhyay, Subhas (Chief Investigator)
- School of Engineering
Project Details
Description
This project will develop a novel sensor assembly, based on neuromorphicsensing, to enable high-speed counting of black soldier fly larvae (BSFL) to
optimise their breeding and production process. BSFLs are used to
process mixed solid waste and convert them into protein rich, high-value
food and feeds ingredient – a critical piece in achieving a circular economy.
ARC Ento Tech have developed an innovative process to treat waste over
the last two years, showing good traction in the industry. However, the
process relies on an optimised production of the flies. Currently the breeding
and production process is based on best practices, but there is a need to
quantify the fly production rate using smart sensors. Traditionally this has
been difficult to achieve due to their small size, yet the extremely high
number (100s of flies per minute). The high-speed counting enabled by
neuromorphic imaging, an expertise of Western Sydney University, will
deliver valuable insights to their processes.
Layman's description
This project will develop a novel sensor assembly, based on neuromorphicsensing, to enable high-speed counting of black soldier fly larvae (BSFL) to
optimise their breeding and production process. BSFLs are used to
process mixed solid waste and convert them into protein rich, high-value
food and feeds ingredient – a critical piece in achieving a circular economy.
| Short title | Fly-count |
|---|---|
| Status | Active |
| Effective start/end date | 1/06/23 → 31/05/24 |
Access Project
- NSSN GC Application form - WSU MQU ARC Ento Tech
File: application/pdf, 981 KB
Type: Application - NSSN GCF23 Outcome Letter - Marcireau
File: application/pdf, 226 KB
Type: Award letter
Technology
The ARC Process™ consists of a continuous series of biological and mechanical processes which converts MSW into marketable commodities namely:- High grade insect meal
- High nutrient fertilizer
- An innovative industrial reductant which can replace coking coal
ARC Ento Tech’s comprehensive approach is the strength of our system.
A Total System Approach to MSW Processing
The key stages of the ARC Process™ use two component core technologies protected under the patent pending registration and form the heart of the system:
1. Bio-Process
ARC’s bio-process utilises the Black Soldier Fly (Hermetia illucens) to consume all edible organic waste in the MSW stream. The larvae are then recovered and converted into commercial products, highly nutrient insect meal and organic fertiliser.
Black Soldier Fly (BSF) Digester
ARC Ento Tech Ltd has developed an innovative continuous process using the Black Soldier Fly to recover nutrients from food waste and digestible organic components.This consists of a ‘digesting circuit’ that provides ideal conditions for the Black Soldier Fly larvae to flourish and optimize nutrient recovery. The recovered nutrients are then re-formed and re-purposed into ‘new’ products that are channeled back into the consumption cycle.
All indigestible organics (such as paper, textile, wood, rubber, leather) and plastics left behind after this process move on to the RDR™ mechanical process which then converts them into another commercial product. This unique process makes for ARC’s total solution to MSW.
LEARN MORE ABOUT THE BSF
2. Mechanical Process
ARC’s mechanical process utilises a unique technology which converts plastics and other non-digestible organics into Refuse Derived Reductant (RDR™), a valuable industrial use product.
Refuse Derived Reductant (RDR™)
ARC Ento Tech Ltd has developed the RDR™ Production System which recovers and re-forms all residual waste consisting of inorganics (plastics) and inedible organics (paper, textiles, wood) into a dense high carbon, high calorific value reducing agent.
Frangipani
Regular
To post or not to post, that is the question…
I am referring to yet another of Nandan Nayampally’s ‘likes’ on LinkedIn:
www.linkedin.com
Is he just giving a friendly thumbs up to his friends and ex-colleagues (“Nayampally comes to BrainChip from Amazon, where he helped accelerate the adoption of Alexa Voice and other multimodal services into third-party devices.”) or should we read more into this?
The move of our CMO from Amazon to Brainchip in November 2022 coincided with the launch of the improved Fire TV voice search, and now a new feedback system is on the horizon. Is it remote-ly possible it has to do with any feedback provided by a former employee?
We’ll have to keep watching the financials, I guess.
CONVERSATIONAL AI / NATURAL-LANGUAGE PROCESSING
June 26, 2023
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Put your hand up if you enjoy using your TV remote to type in the name of the show you want to watch next. Who doesn’t love shuffling the highlighted box across the screen, painstakingly selecting each letter in turn? And let’s not forget the joy of accidentally selecting a wrong letter.
Such text-based search works, but it can feel like a chore. It’s much easier and faster to just ask for what you want. With Amazon’s Fire TV, you can ask the Alexa voice assistant to find your favorite shows, movies, movie genres, actors … you name it.
But voice-based search can come with its own frustrations. What if Alexa misheard a request for the TV show Hunted as “haunted” and as a result presented a spooky screenful of incorrect suggestions?
Related content
The science behind the “Alexa, what should I watch?” feature
This is a story of how two groups at Amazon — the Fire TV Search team and the Alexa Entertainment Spoken Language Understanding team — collaborated to launch an improved Fire TV voice search experience in the U.S. in November 2022.
The new search system gives customers a greater chance of finding what they are looking for, on their first attempt, by casting the search net a little wider — and a little smarter. It works by harnessing a suite of Alexa machine learning (ML) models to generate additional, similar-sounding words to inject into Fire TV’s search function to broaden the scope of the results presented to the customer. Hence its name: phonetically blended results (PBR). Today, about 80% of the 20 million or so unique search terms that Fire TV deals with are augmented by PBR.
(…)
And it will only get better with time. The Alexa and Fire TV teams are working toward a feedback learning system that will allow PBR’s models to automatically generate new search candidates, prune ineffective ones, and home in on increasingly accurate confidence scores (…)
I am referring to yet another of Nandan Nayampally’s ‘likes’ on LinkedIn:
Tapas Roy on LinkedIn: The science behind the improved Fire TV voice search | 11 comments
Very excited about how Alexa and Fire TV collaborated to improve voice search for our customers. https://lnkd.in/g8zqKctN | 11 comments on LinkedIn
Is he just giving a friendly thumbs up to his friends and ex-colleagues (“Nayampally comes to BrainChip from Amazon, where he helped accelerate the adoption of Alexa Voice and other multimodal services into third-party devices.”) or should we read more into this?
The move of our CMO from Amazon to Brainchip in November 2022 coincided with the launch of the improved Fire TV voice search, and now a new feedback system is on the horizon. Is it remote-ly possible it has to do with any feedback provided by a former employee?
We’ll have to keep watching the financials, I guess.
CONVERSATIONAL AI / NATURAL-LANGUAGE PROCESSING
The science behind the improved Fire TV voice search
How phonetically blended results (PBR) help ensure customers find the content they were actually asking for.
By Sean O'NeillJune 26, 2023
Share
Put your hand up if you enjoy using your TV remote to type in the name of the show you want to watch next. Who doesn’t love shuffling the highlighted box across the screen, painstakingly selecting each letter in turn? And let’s not forget the joy of accidentally selecting a wrong letter.
Such text-based search works, but it can feel like a chore. It’s much easier and faster to just ask for what you want. With Amazon’s Fire TV, you can ask the Alexa voice assistant to find your favorite shows, movies, movie genres, actors … you name it.
But voice-based search can come with its own frustrations. What if Alexa misheard a request for the TV show Hunted as “haunted” and as a result presented a spooky screenful of incorrect suggestions?
Related content
The science behind the “Alexa, what should I watch?” feature
This is a story of how two groups at Amazon — the Fire TV Search team and the Alexa Entertainment Spoken Language Understanding team — collaborated to launch an improved Fire TV voice search experience in the U.S. in November 2022.
The new search system gives customers a greater chance of finding what they are looking for, on their first attempt, by casting the search net a little wider — and a little smarter. It works by harnessing a suite of Alexa machine learning (ML) models to generate additional, similar-sounding words to inject into Fire TV’s search function to broaden the scope of the results presented to the customer. Hence its name: phonetically blended results (PBR). Today, about 80% of the 20 million or so unique search terms that Fire TV deals with are augmented by PBR.
(…)
And it will only get better with time. The Alexa and Fire TV teams are working toward a feedback learning system that will allow PBR’s models to automatically generate new search candidates, prune ineffective ones, and home in on increasingly accurate confidence scores (…)
SR6G7 at VLSI 2023, revolutionary auto-grade MCU with 21 MB ePCM that expands to 40.5 MB in full over-the-air mode
<p><strong>ST introduced the SR6G7, its new automotive-grade microcontroller with 21 MB of embedded phase-change memory (ePCM) at the 2023 Symposium on VLSI (very large-scale integration) Technology and Circuits (VLSI 2023 for short).</strong> We also held a demo showcasing how the 21 MB of ePCM...
blog.st.com
AMD says its FPGA is ready to emulate your biggest chips
But can it run Crysis?
Snippet from below text, also on website from cosors post, thanks cosors.
It then becomes far easier for subsequent processing to separate distant objects and to apply machine-learning-based classification algorithms similar to those used with camera or lidar data.
Sorry I don't know how to circle or highlight text
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Millimeter-wave radar expands the operational design domain for autonomous vehicles
By Zeev KaplanJun 27, 2023 08:03am
CEVAradar sensorsself-driving carsanalog to digital converter
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Advances in sensors will allow further advances in autonomous vehicles as the writer points out. (Getty images)
It is becoming clear that further advances in autonomous vehicles will depend to a significant degree on advances in sensors.
To move forward, the vehicles must improve the reliability with which they identify and classify objects, the accuracy with which they sense the motion of those objects, and, as sensor technology improves, the range of environmental conditions—the operational design domain—in which the autonomy systems can operate. All of these needs depend upon the quality of data coming in from the vehicle’s sensor suite.
That suite has mainly included, for L2 and L2+ autonomy, high-resolution optical cameras, plus radars with limited range and field of view. Short-range sonar or radar sensors have also been used for specific comfort and safety functions such as automated parking, smart cruise control, and lane retention. To reach L3, designers are finding they have to include lidar sensors, which bring a host of disadvantages and limitations, not the least of which is high cost.
But a new generation of 4D imaging radar sensors is changing this picture dramatically. These affordable, high-resolution millimeter-wave radar sensors offer excellent resolution in 4 dimensions: range, azimuth, and—for the first time—elevation, plus accurate, directly-measured velocity. The new sensors also provide longer range and wider field of view, and they support extended operational design domain. All of the data gets delivered in real time to the sensor-fusion processor of the autonomous vehicle.
The conjunction of these features greatly increases the opportunity for increased reliability and wider operating domains. Millimeter-wave radar—unlike cameras or lidar—is inherently able to penetrate difficult weather or lighting conditions. And the combination of high spatial resolution and accurate velocity measurement reduces ambiguities in object detection. The result is more robust object detection and classification over a broader range of environmental conditions.
Radar Evolution
These new sensors are very different from most previous-generation automotive radars. Their advantages are based on mature technologies with years of deployment in military and aerospace applications. Now, increasing integration and performance in semiconductors and antenna fabrication have brought these features down in scale and cost, from a system in an F-18 fighter to a compact module that can be installed at multiple points on a passenger car.
The first significant change has been the availability of low-cost millimeter-wave radar transmitter and receiver hardware, offering good transmitter power and high receiver sensitivity. This in turn enables two more changes: the use of dense multi-antenna arrays—known as multiple-in, multiple-out, or MIMO, in 5G communications—and the use of complicated waveforms that greatly enhance elevation, azimuth, and velocity measurement. Finally, advances in digital signal processing (DSP) intellectual property have made it possible to include the numerical processing power necessary to handle the many channels of high-speed data these sensors generate in real time.
Technical Challenges
Just how these evolutionary changes turn into valuable features is a tale about the interaction of hardware capabilities with firmware functionality. For example, in order to get really high resolution in azimuth and elevation, the sensor must use large-aperture, virtual-array synthesis techniques. Advanced signal-processing algorithms can combine many signals across the time, frequency, or code domains, or some combination of these, to create a virtual antenna array much larger than the physical array.
This greatly increases the resolution: current designs target less than one degree in azimuth, and about one degree in elevation. This resolution produces more uncorrelated measurement points per object, better defining the location and outline of objects than is possible with low-resolution radars. It then becomes far easier for subsequent processing to separate distant objects and to apply machine-learning-based classification algorithms similar to those used with camera or lidar data.
In these systems, design of the transmitted waveforms has a huge influence on the performance of the sensor and the overall cost of the entire solution. Many tradeoffs and considerations become important when selecting the basic waveform structure—the transmitted chirp sequence—creating orthogonality between the transmitted waveform and the process of virtual array creation. For instance, transmitting from several transmitter antennas in parallel will produce numerous virtual-channel measurements on the receiver side. It is necessary to take into account the challenges of separating out those measurements with reasonable algorithmic and processing capabilities.
Selecting an approach to channel multiplexing also involves tradeoffs. Imposing limitations on measurements can result in artifacts. For example, such limitations can induce coupling of angular and doppler measurements, influencing the span of supported unambiguous velocity measurements or creating other measurement ambiguities.
There are also many system level implications to mention. To cite only a few of them: The desire to allow high analog bandwidth and short chirp duration introduces a requirement for higher sampling rates. That in turn makes ADC convertor design harder and increases the ADC cost. Another example: phase based channel-multiplexing schemes require analog phase shifters with high phase resolution. But such phase shifters are a fabrication challenge, and they require delicate and sensitive calibration off and on-line. A third point is that transmitting from several transmit antenna elements concurrently will require more complex thermal design at system level to dissipate the additional heat generated by the transmitters.
Altogether, the design considerations required to exploit high-resolution millimeter-wave radar in a vehicle autonomy system are many and non-trivial. But they are more than rewarded by the increased capabilities these sensors bring to the system.
Real-World Advantages
These capabilities are more than just improved numbers on a data sheet. They are functional differences that can translate into increased safety, autonomy, and operational design domain in real-word vehicles, all while driving in real-world situations.
For example, the increased volume and accuracy of 3D position and velocity data can significantly improve the autonomous vehicle’s ability to identify objects. The better data from the sensor allows the vehicle to make delicate but vital distinctions—separating the strong reflected signal from a large truck from the weaker signal from a nearby small child, for example. Real-time Doppler velocity measurements mean, among other things, the ability to detect a sudden change in an object’s velocity at once, rather than after several scans of the field of view, and the ability to separate close-together objects moving at different velocities.
All of these benefits add up to a better understanding of the situation around the vehicle. And that means greater safety. Add to this the ability of high-resolution millimeter-wave radar to operate in conditions of poor visibility and in cluttered scenes with complicated lighting, and you get a vehicle autonomy system that can function with greater safety and reliability across a wider range of conditions: just the benefit for which the industry is striving.
A flexible, scalable solution
All of these challenges call for a flexible and comprehensive solution for a radar SOC that is both “software defined” and scalable to support advanced radar processing algorithms, Such a platform would including high-performance DSP engines, optimized hardware accelerators for multi-dimension FFT operations, and a dedicated software development kit. For example, CEVA’s SensPro sensor hub architecture delivers this combination with a family of DSP products that offers a range processing capabilities, making it well suited to address wide range of customers use-cases and requirements
The suite of products allows developers to create different flavors or generation of products using different SensPro family members. With its common architecture, simple and smooth migration of DSP software code between the cores preserves investment in the previously developed software codebase and shortens time-to-market.
Of course, the underlying processing capabilities must keep pace with emerging needs, and thus a programmable architecture is required. As market requirements have evolved, CEVA has continuously optimized its SensPro architecture and instruction set to support feature such as:
· Reliable and robust target detection using advanced CFAR schemes (e.g. OS-CFAR);
· Support for enhanced resolution, beyond “Fourier limits”, with super-resolution advanced algorithms;
· Support for inter-frame level processing through processing of radar point clouds to implement advance tracking schemes (for example using Kalman filters and application of a dedicated AI models trained to segment and classify objects from “post-tracker” point cloud)
Radar processing chain and SenPro DSP workloads (CEVA)
Beyond Driver Assistance
Today’s level of vehicle autonomy, when used responsibly, can significantly improve both vehicle safety and traffic flow. But the ultimate goal remains full autonomy, at least for some categories of vehicles. High-resolution 4D millimeter-wave radar will be a key part of the solution to that challenge. Designers envision a sensor suite that includes a 4D radar at each corner of the vehicle and at least one lidar, all feeding a sophisticated sensor-fusion processor and AI. With sufficient volume and quality of sensor data, adequate processing power for the fusion and AI stages, and sufficient training, it is hoped, errors will be reduced and operational design domain expanded until fully autonomous vehicles, able to operate in nearly any environment, become an accepted part of the transportation system.
But the future will hold more challenges. As the number of vehicles deploying millimeter-wave radar expands, the chances of interference increase. This will force innovation in the waveform processing in the individual radar sensors, and, one hopes, the emergence of standards for vehicular radar. Standards, in turn, could lead to progress in vehicle-to-vehicle and vehicle-to-infrastructure cooperation, which could mean a whole new role for the radar sensors, as parts of a massive distributed intelligence network.
The current lack of agreed-upon standards in automotive radar transmission requires a solution to have a high level of programmability to adapt. This could include, for example, being able to implement possible interference mitigation on a single sensor level, or the addition of coordination mechanisms between the sensors and the infrastructure, so sensors could utilize the resources of the shared spectrum in a safe and efficient way. Continuing progress in underlying semiconductor technology, antenna design, and algorithm development will also keep pace with these emerging ideas.
And the self-driving passenger car is far from the only application for these new sensors. Obviously, there are many other types of vehicles that can benefit from autonomy or advanced driver assistance over a wide operational design domain, particularly as the size and cost of the sensors declines. But the benefits are equally important for stationary applications, such as traffic flow management and pedestrian safety systems in congested areas. One can imagine cooperative systems in which trucks, cars, motorcycles, bicycles, and pedestrians are in continuous communications about their location, velocity, and what they see around them.
Basically, high-resolution millimeter-wave radar sensors will have applications in any situation where understanding a dynamic environment is important and visual camera data is insufficient. Their technology has more than enough headroom to grow to meet new requirements. The limit to their application may be only the creativity of system designers.
Zeev Kaplan is a Product Director in CEVA's Mobile Broadband business units and has been in the communication industry for more than 15 years.
He joined CEVA in 2011 and was a key player in design and implementation of CEVA Cellular Communication products portfolio spanning from CEVA-XC cores to PentaG HW accelerators. He has been granted several patents in the field of communications and signal processing.and holds advanced degrees from Technion, Israel Institute of Technology in Electrical Engineering.
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MDhere
Regular
" this"series which means its in the other series
Pertinent word is "this" snd refers writer to their website where they can find the correct answer
very clever direction. love it. 
The Benefits of Integrating AI and ML in Robotic Automation Systems
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The Benefits of Integrating AI and ML in Robotic Automation Systems
Robots are intelligent and autonomous machines that can mimic human movements and assist us in various ways. Some robots can resemble humans, while others are robotic applications or bots, such as robotic automation systems, which simulate human interaction with computer applications.
I think that they would want to incorporate either Akida 1500 or Akida Gen 2 into their new products rather than using the original Akida 1.0..... IMO, it is most likely with all our other NDA's, Partners, Customers and Collaborators that they too will want to do same to enhance the commercialisation of their products and brand name.
Interesting use of words here ...That being:
" We've confirmed that this series does not ".
Thus this Implies to me that we may well be in the next generation of this product..... they probably want an updated Akida Gen 2 IP
buena suerte :-)
BOB Bank of Brainchip
Exactly MD ..."this" series (previous!) maybe we are now in the new series!!??" this"series which means its in the other series
Pertinent word is "this" snd refers writer to their website where they can find the correct answer
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