RobjHunt
Regular
Any nuggets of late Fred? We may get another little one at the end of the week
BRN Discussion Ongoing
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Fredsnugget
Regular
Yep, got close to an ounce in nuggets over weekend. A BRN nugget to end this week would be great. Just gotta clean all the ironstone of the specimensAny nuggets of late Fred? We may get another little one at the end of the week
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supersonic001
Regular
Microchip Technology Inc. on LinkedIn: #embeddedcontrol #automotiveinnovation
Unlock the potential of modern vehicles with our trusted embedded control solutions. From analog to connectivity, memory to MCUs, security to timing, weâŚ
I hope you are right. Its been very disappointing. It is taking forever. Patience is running out.
Did someone tell you this, do you hear voices, was it written on stone tablets or did you perhaps have a divine apparition?
Bravo
If ARM was an arm, BRN would be its bicepsđŞ!
A new article from our friends at Socionext.
edge AIagriculturesmart citiesSocionext
Edge AI refers to processing data closer to its source, reducing latency, and enhancing real-time decision-making. Integrating sensors with Edge AI will push IoT innovation. (Socionext)
The proliferation of IoT devices and applications has generated massive data. Integrating sensors with edge AI has emerged as a critical solution to harness the potential of this data effectively.
Edge AI involves processing data closer to its source, reducing latency, and enhancing real-time decision-making. Combining this approach with sensors creates a powerful synergy, allowing devices to perceive, understand, and act autonomously.
Sensor Technologies
Various sensor technologies, such as cameras, temperature sensors, accelerometers, and proximity sensors, are fundamental components of IoT systems. These sensors capture data from the physical world, and their integration with edge AI enhances the capabilities of IoT devices. Sensor data can provide information about the environment, user behavior, and system health, which enables intelligent decision-making when analyzed at the edge.
Edge AI Capabilities
Edge AI brings machine learning and deep learning models to IoT devices, enabling them to process and analyze data locally. This approach provides several advantages, including reduced latency, improved privacy, and more efficient use of network resources. Depending on the AI models deployed, the capabilities of edge AI extend to real-time object detection, anomaly detection, predictive maintenance, and even natural language processing.
Key Benefits of Integrating Sensors with Edge AI
I. Real-time Decision Making: Edge AI enables devices to make decisions in real-time based on sensor data, reducing the need for centralized cloud processing. This is particularly crucial for applications like autonomous vehicles, industrial automation, and healthcare monitoring.
II. Enhanced Security and Privacy: Data remains on the device, minimizing the risk of data breaches and ensuring user privacy. This is vital in applications like home security systems and healthcare devices.
III. Reduced Network Traffic: By processing data at the edge, only relevant information is sent to the cloud, reducing bandwidth requirements and associated costs.
IV. Energy Efficiency: Edge AI optimizes energy consumption by processing data locally, leading to longer battery life for IoT devices.
V. Robustness in Unreliable Connectivity: Edge AI allows devices to function even in scenarios with intermittent or unstable network connections.
VI. Adaptability and Customization: Edge AI models can be tailored to specific use cases, making them adaptable to various applications.
Use Cases
I. Smart Cities: Integrating sensors with edge AI in smart city applications enhances traffic management, public safety, waste management, and environmental monitoring.
II. Industrial IoT: In manufacturing, sensors combined with edge AI improve quality control, predictive maintenance, and worker safety.
III. Healthcare: Wearable devices can provide real-time health monitoring, helping individuals manage chronic conditions and alerting healthcare providers in emergencies.
IV. Agriculture: Sensors on agricultural equipment can optimize planting, irrigation, and harvesting, leading to increased crop yields.
V. Retail: Edge AI-powered cameras and sensors enhance customer experiences through personalized recommendations and efficient inventory management.
Challenges
I. Resource Constraints: IoT devices often have limited computational resources, making it challenging to deploy sophisticated AI models.
II. Data Quality: The accuracy of AI models depends on the quality of the sensor data, which can be affected by environmental conditions.
III. Security: Edge AI devices must be safeguarded against physical and cyber threats, as they often operate in uncontrolled environments.
IV. Model Updates: Updating AI models on edge devices can be complex, and mechanisms for efficient model management must be developed.
Future Directions
The integration of sensors with edge AI is poised for significant growth. Emerging technologies, such as 5G networks and low-power AI hardware, will enable even more sophisticated and efficient edge AI solutions. Developing federated learning techniques will also allow AI models to be updated collectively while maintaining data privacy.
Socionext offers a wide range of low-power 24GHz and 60GHz RF CMOS sensors for detecting angles, distances, and objects. These sensors are suitable for everyday applications that require advanced calibration features.
Combined with the implementation of edge AI technology, Socionext extends benefits across various industries and applications. By deploying AI processing capabilities directly at the edge, closer to the source of data generation, Socionext enhances efficiency, reduces latency, and ensures real-time decision-making. This proves particularly advantageous when low-latency responses are crucial, such as autonomous vehicles, industrial automation, and healthcare. Additional benefits include remote or resource-constrained environments where continuous cloud connectivity may not be feasible.
Integrating sensors with edge AI represents a pivotal step forward in the IoT landscape. Enabling devices to make real-time, autonomous decisions based on sensor data enhances efficiency, security, and the overall user experience. As this technology continues to evolve, it will unlock new opportunities across various domains, reshaping the way we interact with our connected world.
Shreyas Basavaraju is a design engineer at Socionext America with over a decade of experience in prototyping SoCs on FPGAs.
edge AIagriculturesmart citiesSocionextSensors
www.fierceelectronics.com
Integrating sensors with edge AI for enhanced IoT solutions
By Shreyas BasavarajuApr 7, 2024 4:04pmedge AIagriculturesmart citiesSocionext
The proliferation of IoT devices and applications has generated massive data. Integrating sensors with edge AI has emerged as a critical solution to harness the potential of this data effectively.
Edge AI involves processing data closer to its source, reducing latency, and enhancing real-time decision-making. Combining this approach with sensors creates a powerful synergy, allowing devices to perceive, understand, and act autonomously.
Sensor Technologies
Various sensor technologies, such as cameras, temperature sensors, accelerometers, and proximity sensors, are fundamental components of IoT systems. These sensors capture data from the physical world, and their integration with edge AI enhances the capabilities of IoT devices. Sensor data can provide information about the environment, user behavior, and system health, which enables intelligent decision-making when analyzed at the edge.
Edge AI Capabilities
Edge AI brings machine learning and deep learning models to IoT devices, enabling them to process and analyze data locally. This approach provides several advantages, including reduced latency, improved privacy, and more efficient use of network resources. Depending on the AI models deployed, the capabilities of edge AI extend to real-time object detection, anomaly detection, predictive maintenance, and even natural language processing.
Key Benefits of Integrating Sensors with Edge AI
I. Real-time Decision Making: Edge AI enables devices to make decisions in real-time based on sensor data, reducing the need for centralized cloud processing. This is particularly crucial for applications like autonomous vehicles, industrial automation, and healthcare monitoring.
II. Enhanced Security and Privacy: Data remains on the device, minimizing the risk of data breaches and ensuring user privacy. This is vital in applications like home security systems and healthcare devices.
III. Reduced Network Traffic: By processing data at the edge, only relevant information is sent to the cloud, reducing bandwidth requirements and associated costs.
IV. Energy Efficiency: Edge AI optimizes energy consumption by processing data locally, leading to longer battery life for IoT devices.
V. Robustness in Unreliable Connectivity: Edge AI allows devices to function even in scenarios with intermittent or unstable network connections.
VI. Adaptability and Customization: Edge AI models can be tailored to specific use cases, making them adaptable to various applications.
Use Cases
I. Smart Cities: Integrating sensors with edge AI in smart city applications enhances traffic management, public safety, waste management, and environmental monitoring.
II. Industrial IoT: In manufacturing, sensors combined with edge AI improve quality control, predictive maintenance, and worker safety.
III. Healthcare: Wearable devices can provide real-time health monitoring, helping individuals manage chronic conditions and alerting healthcare providers in emergencies.
IV. Agriculture: Sensors on agricultural equipment can optimize planting, irrigation, and harvesting, leading to increased crop yields.
V. Retail: Edge AI-powered cameras and sensors enhance customer experiences through personalized recommendations and efficient inventory management.
Challenges
I. Resource Constraints: IoT devices often have limited computational resources, making it challenging to deploy sophisticated AI models.
II. Data Quality: The accuracy of AI models depends on the quality of the sensor data, which can be affected by environmental conditions.
III. Security: Edge AI devices must be safeguarded against physical and cyber threats, as they often operate in uncontrolled environments.
IV. Model Updates: Updating AI models on edge devices can be complex, and mechanisms for efficient model management must be developed.
Future Directions
The integration of sensors with edge AI is poised for significant growth. Emerging technologies, such as 5G networks and low-power AI hardware, will enable even more sophisticated and efficient edge AI solutions. Developing federated learning techniques will also allow AI models to be updated collectively while maintaining data privacy.
Socionext offers a wide range of low-power 24GHz and 60GHz RF CMOS sensors for detecting angles, distances, and objects. These sensors are suitable for everyday applications that require advanced calibration features.
Combined with the implementation of edge AI technology, Socionext extends benefits across various industries and applications. By deploying AI processing capabilities directly at the edge, closer to the source of data generation, Socionext enhances efficiency, reduces latency, and ensures real-time decision-making. This proves particularly advantageous when low-latency responses are crucial, such as autonomous vehicles, industrial automation, and healthcare. Additional benefits include remote or resource-constrained environments where continuous cloud connectivity may not be feasible.
Integrating sensors with edge AI represents a pivotal step forward in the IoT landscape. Enabling devices to make real-time, autonomous decisions based on sensor data enhances efficiency, security, and the overall user experience. As this technology continues to evolve, it will unlock new opportunities across various domains, reshaping the way we interact with our connected world.
Shreyas Basavaraju is a design engineer at Socionext America with over a decade of experience in prototyping SoCs on FPGAs.
edge AIagriculturesmart citiesSocionextSensors
Ambient IoT is the next wave of IoT technology
The next stage of evolution for the IoT, the Ambient IoT refers to a new class of innovative form-factor designs and low-maintenance or maintenance-free devices primarily powered by harvesting ener | The Bluetooth SIG and ABI Research have established Ambient IoT-related guidelines.
The obligatory question arises:A new article from our friends at Socionext.
Integrating sensors with edge AI for enhanced IoT solutions
By Shreyas BasavarajuApr 7, 2024 4:04pm
edge AIagriculturesmart citiesSocionext
Edge AI refers to processing data closer to its source, reducing latency, and enhancing real-time decision-making. Integrating sensors with Edge AI will push IoT innovation. (Socionext)
The proliferation of IoT devices and applications has generated massive data. Integrating sensors with edge AI has emerged as a critical solution to harness the potential of this data effectively.
Edge AI involves processing data closer to its source, reducing latency, and enhancing real-time decision-making. Combining this approach with sensors creates a powerful synergy, allowing devices to perceive, understand, and act autonomously.
Sensor Technologies
Various sensor technologies, such as cameras, temperature sensors, accelerometers, and proximity sensors, are fundamental components of IoT systems. These sensors capture data from the physical world, and their integration with edge AI enhances the capabilities of IoT devices. Sensor data can provide information about the environment, user behavior, and system health, which enables intelligent decision-making when analyzed at the edge.
Edge AI Capabilities
Edge AI brings machine learning and deep learning models to IoT devices, enabling them to process and analyze data locally. This approach provides several advantages, including reduced latency, improved privacy, and more efficient use of network resources. Depending on the AI models deployed, the capabilities of edge AI extend to real-time object detection, anomaly detection, predictive maintenance, and even natural language processing.
Key Benefits of Integrating Sensors with Edge AI
I. Real-time Decision Making: Edge AI enables devices to make decisions in real-time based on sensor data, reducing the need for centralized cloud processing. This is particularly crucial for applications like autonomous vehicles, industrial automation, and healthcare monitoring.
II. Enhanced Security and Privacy: Data remains on the device, minimizing the risk of data breaches and ensuring user privacy. This is vital in applications like home security systems and healthcare devices.
III. Reduced Network Traffic: By processing data at the edge, only relevant information is sent to the cloud, reducing bandwidth requirements and associated costs.
IV. Energy Efficiency: Edge AI optimizes energy consumption by processing data locally, leading to longer battery life for IoT devices.
V. Robustness in Unreliable Connectivity: Edge AI allows devices to function even in scenarios with intermittent or unstable network connections.
VI. Adaptability and Customization: Edge AI models can be tailored to specific use cases, making them adaptable to various applications.
Use Cases
I. Smart Cities: Integrating sensors with edge AI in smart city applications enhances traffic management, public safety, waste management, and environmental monitoring.
II. Industrial IoT: In manufacturing, sensors combined with edge AI improve quality control, predictive maintenance, and worker safety.
III. Healthcare: Wearable devices can provide real-time health monitoring, helping individuals manage chronic conditions and alerting healthcare providers in emergencies.
IV. Agriculture: Sensors on agricultural equipment can optimize planting, irrigation, and harvesting, leading to increased crop yields.
V. Retail: Edge AI-powered cameras and sensors enhance customer experiences through personalized recommendations and efficient inventory management.
Challenges
I. Resource Constraints: IoT devices often have limited computational resources, making it challenging to deploy sophisticated AI models.
II. Data Quality: The accuracy of AI models depends on the quality of the sensor data, which can be affected by environmental conditions.
III. Security: Edge AI devices must be safeguarded against physical and cyber threats, as they often operate in uncontrolled environments.
IV. Model Updates: Updating AI models on edge devices can be complex, and mechanisms for efficient model management must be developed.
Future Directions
The integration of sensors with edge AI is poised for significant growth. Emerging technologies, such as 5G networks and low-power AI hardware, will enable even more sophisticated and efficient edge AI solutions. Developing federated learning techniques will also allow AI models to be updated collectively while maintaining data privacy.
Socionext offers a wide range of low-power 24GHz and 60GHz RF CMOS sensors for detecting angles, distances, and objects. These sensors are suitable for everyday applications that require advanced calibration features.
Combined with the implementation of edge AI technology, Socionext extends benefits across various industries and applications. By deploying AI processing capabilities directly at the edge, closer to the source of data generation, Socionext enhances efficiency, reduces latency, and ensures real-time decision-making. This proves particularly advantageous when low-latency responses are crucial, such as autonomous vehicles, industrial automation, and healthcare. Additional benefits include remote or resource-constrained environments where continuous cloud connectivity may not be feasible.
Integrating sensors with edge AI represents a pivotal step forward in the IoT landscape. Enabling devices to make real-time, autonomous decisions based on sensor data enhances efficiency, security, and the overall user experience. As this technology continues to evolve, it will unlock new opportunities across various domains, reshaping the way we interact with our connected world.
Shreyas Basavaraju is a design engineer at Socionext America with over a decade of experience in prototyping SoCs on FPGAs.
edge AIagriculturesmart citiesSocionextSensors
Ambient IoT is the next wave of IoT technology
The next stage of evolution for the IoT, the Ambient IoT refers to a new class of innovative form-factor designs and low-maintenance or maintenance-free devices primarily powered by harvesting ener | The Bluetooth SIG and ABI Research have established Ambient IoT-related guidelines.
Brainchip Akida inside ???
Actually I have a
! Bought in Vietnam on the Mekong River from an old lady⌠donât want to tell the whole story again.There's a fair chance:
sn_pr20221223_01e.pdf (socionext.com)
[MILPITAS/Calif. and Yokohama/Japan. December 23, 2022] --- Socionext Inc., a global leader in the design and development of innovative System-on-Chip products, will showcase its automotive custom SoC technologies at the CES Vehicle Tech and Advanced Mobility Zone, located in the Las Vegas Convention Center, North Hall, Booth 10654. CES runs from January 5-8, 2023.
Advanced AI Solutions for Automotive
Socionext has partnered with artificial intelligence provider BrainChip to develop optimized, intelligent sensor data solutions based on Brainchipâs AkidaÂŽ processor IP.
BrainChipâs flexible AI processing fabric IP delivers neuromorphic, event-based computation, enabling ultimate performance while minimizing silicon footprint and power consumption. Sensor data can be analyzed in real time with distributed, high-performance and low-power edge inferencing, resulting in improved response time and reduced energy consumption.
Creating a proprietary chip requires a complex, highly structured framework with a complete support system for addressing each phase of the development process. With extensive experience in custom SoC development, Socionext uses state-of-the-art process technologies, such as 7nm and 5nm , to produce automotive-grade SoCs that ensure functional safety while accelerating software development and system verification.
Socionext is committed to offering the optimal combination of IPs, design expertise, software development and support to implement large-scale, fully customizable automotive SoC solutions to meet the most demanding and rigorous automotive application performance requirements.
Akida - Gateway to the Cyberverse
ndefries
Regular
Smoothsailing
Regular
Orders in âŚbank account full
As I saidâŚ. NO FINANCIAL ADVICE!!!!
Iâm patient to be honest. All the prognosis for our Market are looking promising! The question here for me personally is not if, itâs when! And I expect a movement from erliest end 2024 beginning 2025. Latest end 2025. MOO DYOR
Smoothsailing
Regular
I believe Sean stated 2024 we needed to have runs on the board to be successful
To me that means hitting 6 figures all round smashing it out of the park .
To me that means hitting 6 figures all round smashing it out of the park .
toasty
Regular
Been hearing dates, and hearing about them blowing out, since 2016..............still believe in the tech, don't have confidence in management to make it happen commercially. Too many timelines blown out and too many promises/comments not met..........
I fully understand your point. Itâs hard to understand whatâs happening behind closed doors. All the speculations with âakida insideâ do the rest. People are too much hyped and on the end they are disappointed. Thatâs why i always think âhumâ when I see postings about âneural linkâ âintelâ âBenzâ which are zero related to brainchip. Nothing but speculations. But yeah.. itâs everyoneâs choice how to handle the informations and how to coordinate his investment. My investment horizon is minimum 10 years
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toasty
Regular
Ten year horizon, yeah me too. Difference is I've already been in 8 years and heard management make many statements (J-curve sales, explosion of sales, watch the financials, etc.) that have not resulted in any demonstrable commercial progress............
Damo4
Regular
Hi Toasty,
Would you consider the lack of continuous IP sales as the cause of those unkept promises?
Would you also lay the blame solely on management, or does the excuses of headwinds soften the blow?
Finally would you be more content if they hadn't made any predictions at all? Would the 8 year wait be ok if they said they are still building an ecosystem and left it at that?
I only ask, as it appears they have modified their plan as they encountered these headwinds. It doesn't sound like there plan was to focus on bolstering partnerships, patents, collaborations etc. They also didn't account for the economic headwinds, rightly or wrongly. What was chips, became IP only and now it's a hybrid model with the money still on IP but using chips to grow the customer base.
The lack of IP sales is definitely a measure of commercial success, or lack thereof, but I do think there's lot of success in other ways that may have laid a solid foundation for when the mainstream market decide to take edge Ai seriously.
Smoothsailing
Regular
Seanâs plan hasnât changed from what I can see with patients, partners, eco systems all in order.
I donât think the IP deals are all Sean plans have ever been.
We are getting close , I take my hat off to those whom have waited along time like myself, this year we win 100% imo.
I donât think the IP deals are all Sean plans have ever been.
We are getting close , I take my hat off to those whom have waited along time like myself, this year we win 100% imo.
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