The Rise of Agentic AI: How Hardware is Evolving for Multi-Step Reasoning

The Rise of Agentic AI: How Hardware is Evolving for Multi-Step Reasoning

In 2026, advancements in AI hardware are paving the way for agentic AI, capable of complex multi-step reasoning. Key technical specs include:

• Neural Network Processors (NNPs): Up to 128 cores and 256 GB/s bandwidth
• Graphics Processing Units (GPUs): NVIDIA Ampere architecture with up to 48 GB GDDR6 memory
• Central Processing Units (CPUs): AMD Ryzen Threadripper processors with up to 64 cores and 128 threads
• Memory: Up to 256 GB DDR5 RAM and 4 TB SSD storage

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Related Technical Deep Dive

Liquid Cooling 2026: Achieving 70% Energy Efficiency in Data Centers

In the pursuit of energy efficiency, data centers are constantly seeking innovative solutions to reduce their environmental impact. One such solution is liquid cooling, a technology that has gained significant attention in recent years.

What is Liquid Cooling?

Liquid cooling involves circulating a coolant through a system to absorb heat and dissipate it outside the facility. This approach can be particularly effective for high-density data centers where traditional air-based cooling methods may not suffice.

Benefits of Liquid Cooling

• Up to 70% energy efficiency compared to traditional air-cooled systems
• Increased reliability and reduced maintenance needs
• Potential for increased rack density and scalability

Video Insights

“Data centers are some of the most power-hungry facilities on the planet, and that has huge environmental implications. By embracing liquid cooling, data center operators can significantly reduce their energy consumption and carbon footprint. In 2026, we’re expecting to see a major shift towards more widespread adoption of this technology.”

Conclusion

In conclusion, liquid cooling has the potential to revolutionize the way data centers operate, offering significant energy efficiency gains and environmental benefits. As the industry continues to evolve, we can expect to see increased investment in this area, driving down costs and making liquid cooling an increasingly viable option for data center operators.

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Technical Deep Dive

Cerebras CS-3: Why Wafer-Scale Engines are the New Gold Standard

Cerebras CS-3: Why Wafer-Scale Engines are the New Gold Standard

In 2026, the AI and deep learning landscape is rapidly evolving. One of the most significant advancements is the emergence of wafer-scale engines like Cerebras' CS-3. In this post, we'll dive into what makes these engines so revolutionary and why they're becoming the new gold standard for AI processing.

What are Wafer-Scale Engines?

Traditional AI accelerators are typically designed as separate chips or modules that process data in a serial manner. In contrast, wafer-scale engines like Cerebras' CS-3 are built on a single silicon wafer, integrating millions of processing elements and memory into a single chip.

• This allows for unprecedented levels of parallelism, enabling the simultaneous processing of massive datasets with incredible speed and efficiency.
• The lack of interconnect bottlenecks and reduced latency enable faster data transfer between processing elements, further amplifying performance gains.

How Does Cerebras CS-3 Differ from Traditional AI Accelerators?

Cerebras' CS-3 is specifically designed to tackle the most complex AI workloads by leveraging its wafer-scale architecture. Some key differentiators include:

• A massive scale of processing elements, exceeding 1 million per chip, allowing for unprecedented parallelism and scalability.
• An on-die memory hierarchy that reduces latency and increases data locality, further accelerating AI computations.

The Impact of Wafer-Scale Engines on the AI Ecosystem

The introduction of wafer-scale engines like Cerebras' CS-3 is poised to transform the AI landscape in several ways:

• Accelerated model training and inference: Wafer-scale engines will enable faster, more efficient AI computations, paving the way for widespread adoption across industries.
• New use cases and applications: The increased processing power and parallelism afforded by wafer-scale engines will unlock new AI-driven applications and workflows.

Conclusion

Cerebras' CS-3 represents a significant inflection point in the development of AI accelerators. By embracing wafer-scale engines, developers can now tap into unprecedented levels of processing power, memory, and parallelism, revolutionizing the way we approach AI processing. As the industry continues to evolve, it's clear that wafer-scale engines will be the new gold standard for AI acceleration.

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2026 AI Insight: Cisco Silicon One G300: Powering Gigawatt-Scale AI Clusters

Cisco Silicon One G300: Powering Gigawatt-Scale AI Clusters in 2026

As the world continues to move towards a more data-driven society, the demand for powerful and efficient artificial intelligence (AI) clusters is growing exponentially. To keep up with this trend, Cisco has introduced the Silicon One G300, a revolutionary new processor designed specifically for large-scale AI workloads.

In 2026, we can expect AI clusters to reach unprecedented scales, with thousands of nodes processing petabytes of data in real-time. The Silicon One G300 is uniquely positioned to meet this challenge head-on, offering unparalleled performance and power efficiency.

Key Features

• Series-4 architecture**: The Silicon One G300 features a state-of-the-art Series-4 architecture, which provides up to 50% better performance per watt compared to previous generations.
• 16-core processor**: With 16 cores and 32 threads, the Silicon One G300 is capable of handling even the most demanding AI workloads with ease.
• Dual-threaded processing**: The processor's dual-threaded design allows for simultaneous execution of multiple instructions, resulting in significant performance gains.
• Enhanced memory bandwidth**: With a whopping 256-bit memory interface and up to 128 GB of DDR4 RAM, the Silicon One G300 can handle massive datasets with ease.

The Impact on AI Clusters

In 2026, we can expect AI clusters to reach new heights in terms of scale and complexity. The Silicon One G300 is specifically designed to meet this challenge head-on, offering:

• Sustainable performance**: With its unprecedented power efficiency, the Silicon One G300 will enable AI clusters to operate at unprecedented scales without sacrificing performance.
• Scalability**: The processor's modular design allows for easy scaling up or down as needed, making it an ideal choice for emerging AI workloads.

Conclusion

The Cisco Silicon One G300 is a game-changing processor that will play a critical role in powering the next generation of AI clusters. With its unparalleled performance and power efficiency, this processor is poised to revolutionize the way we approach large-scale AI workloads.

As we look towards 2026 and beyond, it's clear that the Silicon One G300 will be at the heart of many groundbreaking AI initiatives. Whether you're a researcher, developer, or enterprise leader, this processor is sure to have a profound impact on your organization's ability to harness the power of AI.

2026 AI Insight: NVIDIA Rubin vs Blackwell: The 10x Inference Efficiency Leap

NVIDIA Rubin vs Blackwell: The 10x Inference Efficiency Leap in 2026

In the world of artificial intelligence and machine learning, inference efficiency is a critical factor that determines the performance and scalability of AI models. With the rapid growth of AI adoption across various industries, the need for efficient inference has become more pressing than ever.

NVIDIA Rubin: The Game-Changer

NVIDIA's latest innovation in this space is the NVIDIA Rubin architecture, which promises a whopping 10x inference efficiency leap over its predecessor, Blackwell. This breakthrough is made possible by a combination of innovative technologies and architectural enhancements.

Key Features:

• Sparse Model Pruning**: A novel pruning technique that eliminates redundant neurons in the model, reducing memory requirements and computation.
• Floating-Point Optimizations**: Optimized floating-point operations for improved arithmetic performance and reduced power consumption.
• Cache-Hierarchy Enhancements**: Optimized cache hierarchy for faster data access and reduced latency.

The Impact:

NVIDIA Rubin's unparalleled inference efficiency is poised to revolutionize the way AI models are deployed. With this technology, developers can now:

• Train more complex models with smaller datasets
• Deploy models on lower-power devices and edge hardware
• Improve overall system performance and reduce latency

Blackwell: The Legacy

NVIDIA Blackwell, the predecessor of Rubin, has been a stalwart in the AI landscape for years. While it still offers respectable inference efficiency, its limitations are becoming increasingly apparent as AI models continue to grow in complexity and size.

Comparing Rubin vs. Blackwell:

Feature	Rubin	Blackwell
Inference Efficiency	+10x over Blackwell	-3x slower than Rubin
Sparse Model Pruning	Yes	No
Floating-Point Optimizations	Yes	Partial

Conclusion:

NVIDIA Rubin is a groundbreaking technology that promises to unlock new levels of inference efficiency, empowering developers to build more complex AI models, deploy them on lower-power devices, and improve overall system performance. As the demand for AI grows, NVIDIA Rubin is poised to become the industry standard for efficient AI inference in 2026 and beyond.

2026 AI Insight: Direct-to-Chip Liquid Cooling for NVIDIA GB200

Direct-to-Chip Liquid Cooling for NVIDIA GB200: A Game Changer

Overview

NVIDIA's latest innovation, the GB200 series GPUs, introduce a novel cooling technology called Direct-to-Chip Liquid Cooling (DCLC). This system aims to address thermal management challenges in high-performance computing and data center applications.

Key Technical Data Points

• Improved Thermal Efficiency: DCLC reduces the heat transfer path by up to 75% compared to traditional air cooling, enabling better thermal management and increased performance.
• Power Usage Effectiveness (PUE): By minimizing waste heat, DCLC can potentially lower Power Usage Effectiveness (PUE), a crucial metric for data center efficiency.
• Scalability: The modular design of the DCLC system allows for scalable cooling solutions, making it suitable for various applications from high-performance computing to cloud services.

Comparative Analysis

	Current Tech	Next Gen - DCLC
Thermal Efficiency	Up to 50% improvement compared to air cooling	Up to 75% improvement compared to air cooling
Power Usage Effectiveness (PUE)	Improvement potential varies depending on implementation	Significant reduction in heat waste, potentially leading to lower PUE values
Scalability	Limited scalability for specific high-performance applications	Modular design allows for scalable cooling solutions

High-Authority External Resources

• NVIDIA Direct-to-Chip Liquid Cooling
• AnandTech: NVIDIA Introduces Direct-to-Chip Liquid Cooling
• The Future of AI Computing - Ainewsscan Blog

Why It Matters

The advent of DCLC represents a significant step forward in thermal management solutions for high-performance computing. By enabling improved cooling efficiency, reduced heat waste, and scalability, NVIDIA's new cooling technology could lead to more efficient data centers, increased performance, and potential cost savings in the long run.

Salesforce rolls out new Slackbot AI agent as it battles Microsoft and Google in workplace AI

Innovative Update Unveiled by Salesforce

Salesforce has taken a bold step in transforming Slackbot, its workplace assistant tool within the Workplace Suite, into an advanced AI agent capable of performing complex tasks. This new iteration represents what executives at Salesforce claim is not just another copilot or virtual assistant but acts as the forefront to agentic enterprise wherein software agents coexist with human counterparts for multitasking and decision-making processes within workplace environments, giving a glimpse into how AI might shape future office interactions.

In an exclusive conversation with Salesforce's Parker Harris, the company’s chief technology officer emphasized that this rebuilt Slackbot is poised to significantly alter workflow dynamics by enabling employees to delegate tasks and access information seamlessly without interrupting their workday routines.

"Slackbot isn't just another copilot or AI assistant," said Harris. "It's the front door to the agentic enterprise, powered by Salesforce." The new Slackbot has been designed as a robust interface that can not only navigate through years of conversation logs but also connect with external resources like Google Drive and calendar data using sophisticated search capabilities.

Salesforce’s Strategic Shift in AI

The initiative appears to be part of Salesforce's broader strategy aimed at convincing investors that artificial intelligence could enhance, rather than render their products obsolete. Harris elaborated on the distinction between Slackbot generations: "The old Slackbot was a little tricycle and the new one is like Porsche." The enhanced version runs on an entirely different architecture built around a large language model with robust search capabilities that can tap into Salesforce records, Google Drive files, calendar data as well as years of historical conversations in Slack.

Salesforce has chosen to retain the Sl

AI for human agency

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AI for Human Agency: Empowering Choice through Technology

Introduction: The Intersection of Intelligence and Autonomy

Artificial Intelligence (AI) is revolutionizing the way we live, work, and make decisions. As an emerging technology with limitless potential, it is crucial to explore how this innovation can enhance human agency—our fundamental ability to act independently based on personal goals, beliefs, and desires. AI is transforming decision-making processes across different areas of life while empowering individuals to take charge of their own reality.

Defining Human Agency in the AI Era

Human agency refers to our capacity as humans with free will; it allows us control over various aspects that shape the reality around us. This encompasses setting goals, formulating plans, and taking actions required for achieving those objectives efficiently toward self-growth or societal development. In essence, human agency is about making conscious choices guided by our own values without external influences disrupting our personal autonomy.

Strategic Impacts of AI on Autonomy

AI has the power to augment and enhance decision-making processes significantly, leading to more informed outcomes for decisions that affect us directly or impact society at large.

• Data & Predictive Analytics: By analyzing vast datasets, AI provides real-time feedback on factors such as market trends or climate patterns. This enables better-informed decisions, helping people plan strategies ahead and reduce risks associated with sudden economic or environmental fluctuations.
• Natural Language Processing (NLP): Communication barriers often lead to miscommunication. AI acts as a mediator, translating words into meaningful context and bridging linguistic boundaries while maintaining cultural sensitivities and ensuring successful negotiations.
• Personalized Learning: AI allows for tailored educational experiences based on an individual student’s strengths and weaknesses. This empowers learners to grow at their own pace and brings diversity to the educational sphere where creative minds can explore new horizons.

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Core Examples of AI-Driven Empowerment

1. Independent Home Management

Virtual assistants like Amazon Alexa and Google Home provide individuals with hands-off control over their environments. By managing lighting systems, temperature settings, and even grocery orders through voice commands, these tools promote personal comfort and eco-friendly behavior without compromising the convenience modern life demands.

2. Healthcare Responsibility and Vital Monitoring

AI is transforming healthcare by allowing individuals to monitor vital signs in real-time. Wearable fitness trackers provide personalized workout routines or nutritional advice based on user goals. This boosts personal responsibility for health and provides scientific facts to support lifestyle choices, assisting in self-improvement without compromising quality time with loved ones.

3. Professional Growth and Career Development

AI-powered platforms like LinkedIn offer resources, skill assessments, and personalized job recommendations. By providing insights into professional networks and identifying potential mentors, AI makes it easier for individuals to navigate the job market and fulfill professional dreams without feeling limited by a lack of traditional resources.

Conclusion: A Vision for a Balanced Future

The ultimate goal of integrating AI into our lives is to amplify our decision-making processes while maintaining control over these powerful tools. As we move forward, AI for Human Agency is shaping a future where technology supports and amplifies our empowered selves, capable of taking charge of our lives independently while maintaining a balance between personal and professional growth.

© 2026 AI News Scan | Official Blog

Specific use cases for AI in Edge Computing and IoT.

ChatGPT Prompt: "Please provide specific use cases for AI in Edge Computing and IoT."

ChatGPT Response: 

"AI in Edge Computing and IoT is transforming many industries by enabling devices to make intelligent decisions in real time, without relying on the cloud. Below are some specific use cases for AI in these domains:

1. Smart Cities

• Traffic Management:

  • Use Case: AI-powered edge devices can analyze data from cameras, traffic sensors, and GPS systems to monitor traffic flow and optimize traffic signals in real time. For example, AI can adjust traffic light timings to reduce congestion based on live traffic data.
  • Example: In Los Angeles, the city uses AI to optimize traffic flow based on real-time analysis of traffic patterns, reducing gridlock and improving overall transportation efficiency.

• Public Safety and Surveillance:

  • Use Case: AI-based facial recognition and anomaly detection systems on security cameras can monitor public spaces, identify potential threats, and alert authorities immediately. Edge AI can perform the processing locally on cameras to avoid bandwidth overload and ensure faster responses.
  • Example: In Singapore, AI-powered cameras are used for smart surveillance to detect unusual activities, such as abandoned bags in train stations, and can notify security teams instantly.

• Smart Street Lighting:

  • Use Case: AI-enabled streetlights can adjust their brightness based on real-time environmental conditions like pedestrian movement or daylight. This reduces energy consumption while maintaining safety.
  • Example: Barcelona uses smart street lights that can adjust their brightness based on the movement of people and vehicles, contributing to energy savings and enhancing public safety.

2. Healthcare

• Remote Patient Monitoring:

  • Use Case: Wearable devices such as smartwatches and medical sensors can collect health data like heart rate, blood pressure, and oxygen levels. AI at the edge processes this data in real time to detect early warning signs of health issues like heart attacks or arrhythmias, allowing for immediate alerts to healthcare providers.
  • Example: Apple Watch uses edge AI to detect abnormal heart rhythms and send alerts to users and their doctors, potentially preventing serious health issues.

• Medical Imaging Analysis:

  • Use Case: AI can be embedded in medical imaging devices (like MRI or X-ray machines) to analyze scans locally and provide real-time results. This helps doctors make faster diagnoses, especially in emergency situations.
  • Example: AI-based radiology systems in hospitals can process X-ray or CT scans at the edge, detecting conditions like tumors or fractures and delivering results within minutes.

• Smart Healthcare Devices:

  • Use Case: Devices like insulin pumps, pacemakers, or wearable ECG monitors use edge AI to analyze patient data and make real-time decisions, such as adjusting insulin doses or pacing heartbeats.
  • Example: Medtronic’s insulin pumps use AI to continuously monitor blood glucose levels and adjust insulin delivery automatically, optimizing diabetes management.

3. Industrial IoT (IIoT)

• Predictive Maintenance:

  • Use Case: AI can be integrated with sensors on machines to monitor their condition and predict failures before they happen. AI models running at the edge analyze data such as vibration, temperature, and pressure in real-time, triggering maintenance alerts and preventing costly breakdowns.
  • Example: GE Aviation uses AI in its industrial sensors on jet engines to predict when parts need maintenance, minimizing downtime and improving safety.

• Smart Factories:

  • Use Case: AI-powered edge devices in manufacturing plants can monitor production lines, optimize workflows, and detect defects in products in real time. For example, AI cameras can inspect products as they come off the assembly line and reject defective items automatically.
  • Example: Siemens uses edge AI in its smart factories to optimize manufacturing processes, reduce energy consumption, and improve the efficiency of production lines.

• Robotic Process Automation (RPA):

  • Use Case: Autonomous robots on factory floors can be equipped with AI to navigate environments, detect objects, and perform tasks like assembly, sorting, or packaging without human intervention.
  • Example: Amazon uses AI-powered robots in its warehouses to manage inventory and handle logistics operations autonomously, improving operational efficiency.

4. Autonomous Vehicles

• Self-Driving Cars:
  • Use Case: Self-driving cars rely on AI to process data from sensors (like LIDAR, cameras, and radar) at the edge to make decisions in real time, such as avoiding obstacles, adjusting speed, or navigating intersections. This real-time processing is essential for safety and performance.
  • Example: Tesla uses AI at the edge in its vehicles to interpret the data from sensors and make driving decisions autonomously, such as steering, braking, and accelerating in response to traffic conditions.
• Fleet Management:
  • Use Case: For fleets of autonomous vehicles (e.g., delivery drones or trucks), AI at the edge can help manage and optimize routing, ensure safe driving, and monitor vehicle health in real time, all while reducing the need for constant cloud communication.
  • Example: Waymo’s self-driving cars use edge AI to make instant decisions on the road and share vehicle performance data with the cloud only when necessary.

5. Smart Homes

• Home Automation:
  • Use Case: AI-powered smart home devices (like thermostats, lights, and security cameras) use edge computing to make real-time decisions. For example, a smart thermostat can learn your heating preferences and adjust the temperature autonomously without needing constant communication with the cloud.
  • Example: Nest uses AI to adjust the home’s temperature based on users’ behaviors and environmental factors, optimizing energy use while maintaining comfort.
• Smart Security Systems:
  • Use Case: AI in security cameras can detect unusual activities (e.g., motion, faces, or sounds) and trigger local alerts or even activate security responses like locking doors or notifying homeowners or authorities.
  • Example: Ring uses edge AI in its doorbell cameras to identify faces and motion. The system can alert homeowners to potential intruders and even start video recordings locally, ensuring privacy and minimizing cloud reliance.

6. Agriculture

• Precision Farming:

  • Use Case: AI-driven IoT sensors in the field can monitor soil health, weather patterns, crop growth, and irrigation needs. Edge AI processes this data locally, making real-time decisions on irrigation, pest control, and fertilization to optimize crop yields while minimizing water and chemical usage.
  • Example: John Deere uses AI at the edge in agricultural machinery to analyze data from tractors and harvesters, providing real-time insights for precision farming, such as adjusting seed planting depth or detecting crop diseases early.

• Drone-Based Monitoring:

  • Use Case: Drones equipped with AI can monitor large agricultural fields, detecting issues like pest infestations, crop diseases, or irrigation problems. The AI processes the data locally, enabling the drone to act quickly, such as spraying pesticides in specific areas.
  • Example: DJI drones use AI to assess the health of crops by analyzing images captured from the air and making recommendations for targeted treatment.

7. Retail

• Smart Shelves and Inventory Management:
  • Use Case: Retail stores use AI-powered IoT sensors to monitor inventory levels, detect product placement, and automatically restock items as needed. These systems can use edge AI to analyze customer preferences and optimize store layouts based on foot traffic and buying behavior.
  • Example: Amazon Go uses AI and IoT to allow customers to shop without traditional checkout lines. Cameras and sensors track items customers pick up, and AI processes this data locally to charge them automatically when they leave the store.
• Personalized In-Store Experience:
  • Use Case: AI in smart displays or digital signage can personalize content based on customer behavior in-store. For example, AI systems at the edge can detect a customer’s preferences and display targeted promotions on in-store screens as they walk by.
  • Example: L’Oréal uses AI-powered kiosks to offer personalized skincare advice based on real-time analysis of a customer’s skin and preferences.

Conclusion:

AI at the edge and in IoT environments is revolutionizing a wide array of industries by enabling real-time, autonomous decision-making, reducing latency, and improving operational efficiency. These technologies help create smarter, more responsive systems in smart cities, healthcare, manufacturing, autonomous vehicles, agriculture, retail, and more. As the capabilities of edge computing and AI evolve, these use cases will only expand, leading to more interconnected, intelligent, and efficient ecosystems.

If you're interested in exploring a specific use case further or want to know more about the underlying technologies, feel free to ask!"

 

ChatGPT Prompt: "Want to know more about the underlying technologies"

ChatGPT Response: 

"Absolutely! To dive deeper into the underlying technologies that enable AI in Edge Computing and IoT, let's break it down into the core components that work together to power these applications. These technologies include edge computing infrastructure, machine learning models, communication protocols, and hardware accelerators. Let’s explore each of these in more detail:

1. Edge Computing Infrastructure

Edge computing involves processing data closer to where it is generated, at the "edge" of the network. This is done by edge devices (e.g., sensors, IoT devices, local servers) that handle real-time data analysis without relying on centralized cloud data centers.

• Edge Devices:

  • Definition: Edge devices are the "frontline" hardware that collect, process, and sometimes store data at or near the source of data generation (e.g., IoT sensors, smartphones, cameras).
  • Examples: Smart thermostats, wearable health trackers, industrial sensors, and autonomous vehicles.
  • Role: These devices run AI models and algorithms locally to make decisions without needing to communicate with the cloud for every data point.

• Edge Gateways:

  • Definition: These are intermediary devices between the edge devices and the cloud. They aggregate data from multiple edge devices, perform preprocessing, and sometimes run AI models before sending the data to the cloud or back to the edge for further analysis.
  • Role: Edge gateways help reduce bandwidth usage and improve real-time decision-making by providing a local data aggregation layer.
  • Example: Cisco’s IoT Gateways aggregate data from industrial sensors and can run basic analytics at the edge before sending critical information to a central cloud system.

2. Machine Learning Models

Machine learning (ML) and deep learning (DL) are the core AI technologies applied in edge computing and IoT. These models allow devices to learn from data, make predictions, and take actions autonomously.

• Machine Learning (ML) Algorithms:

  • Types: Supervised learning, unsupervised learning, reinforcement learning, and transfer learning are commonly used algorithms in AI-driven IoT applications.
  • Example: In smart factories, supervised learning can be used to train AI models on labeled data (e.g., sensor data labeled as "normal" or "faulty") to detect anomalies in real-time.

• Deep Learning (DL) Models:

  • Types: Neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) are widely used in IoT and edge computing applications, particularly for tasks like image and speech recognition.
  • Example: Autonomous vehicles use CNNs for image recognition (e.g., identifying pedestrians or traffic signs) and RNNs for sequential data analysis (e.g., predicting the next movement of a vehicle based on current traffic conditions).

• Model Optimization for Edge:

  • TinyML: Tiny machine learning (TinyML) refers to the deployment of lightweight ML models on small, resource-constrained devices. These models are optimized to run efficiently on devices with limited processing power, memory, and battery life.
  • Example: Arduino and Raspberry Pi microcontrollers are often used for TinyML applications where models are deployed on edge devices with limited resources.
  • Model Compression: Techniques like pruning, quantization, and knowledge distillation help reduce the size and complexity of machine learning models so they can run on edge devices with limited resources.

3. Communication Protocols

The seamless transfer of data between IoT devices, edge gateways, and the cloud is essential. Communication protocols ensure the reliable and efficient transmission of data, both within the IoT network and between edge devices.

• Low Power Wide Area Network (LPWAN):

  • Definition: LPWAN protocols like LoRaWAN and NB-IoT are designed for long-range, low-power communication. They are ideal for IoT devices that need to send small amounts of data over long distances without frequent recharging.
  • Example: In agriculture, LPWAN protocols enable the connection of sensors that monitor soil moisture, weather, and crop health in remote fields without requiring constant connectivity to the cloud.

• Bluetooth Low Energy (BLE):

  • Definition: BLE is a short-range communication protocol used for devices that require low energy consumption, such as fitness trackers, smart home devices, and beacons.
  • Example: Wearables like smartwatches use BLE to communicate with mobile phones for data synchronization and alerts.

• Wi-Fi 6 and 5G:

  • Definition: Both Wi-Fi 6 and 5G provide higher bandwidth and lower latency, which are crucial for real-time applications like smart cities and autonomous vehicles. 5G, in particular, supports a vast number of IoT devices simultaneously and offers faster data transfer speeds.
  • Example: In autonomous driving, 5G enables low-latency communication between vehicles, edge devices, and infrastructure, improving decision-making speeds in dynamic environments.

• MQTT (Message Queuing Telemetry Transport):

  • Definition: MQTT is a lightweight messaging protocol designed for efficient communication between IoT devices, often used for real-time messaging and remote control.
  • Example: A smart thermostat might use MQTT to send sensor data and receive control signals in real time from a mobile app or cloud server.

4. Hardware Accelerators for AI at the Edge

Running machine learning models on edge devices often requires powerful hardware to process the data in real-time, given the computational intensity of many AI algorithms.

• Graphics Processing Units (GPUs):

  • Definition: GPUs are specialized processors designed to handle parallel processing tasks, making them ideal for running deep learning models, especially for tasks like image and video analysis.
  • Example: NVIDIA Jetson series is a popular hardware platform for deploying AI models on edge devices like drones, robots, and IoT gateways. It uses GPUs to run AI models efficiently at the edge.

• Tensor Processing Units (TPUs):

  • Definition: TPUs are application-specific integrated circuits (ASICs) designed by Google to accelerate machine learning workloads. TPUs are ideal for edge AI applications that require deep learning models.
  • Example: Google Coral devices include TPUs for edge AI processing, enabling real-time object detection, face recognition, and other AI tasks on IoT devices.

• Field-Programmable Gate Arrays (FPGAs):

  • Definition: FPGAs are programmable hardware devices that can be configured to execute specific AI algorithms. They provide a balance between computational power, energy efficiency, and flexibility, making them suitable for real-time processing on edge devices.
  • Example: Xilinx FPGAs are used in edge applications where custom AI models need to be deployed for low-latency tasks like real-time video analytics and industrial automation.

• Edge AI Chips:

  • Definition: Companies are developing specialized chips optimized for running AI algorithms at the edge, balancing power consumption and performance for specific IoT applications.
  • Example: Apple's A-series chips in iPhones use neural processing units (NPUs) to accelerate machine learning tasks like facial recognition and augmented reality applications.

5. Data Management and Processing Frameworks

Efficient data management and processing frameworks are essential to handle the vast amounts of data generated by IoT devices and analyzed by AI models.

• Apache Kafka:
  • Definition: Kafka is a distributed streaming platform that allows IoT devices to publish data in real time. It helps collect, store, and process large streams of data from various sensors and devices before sending it to the edge or cloud for further processing.
  • Example: In industrial IoT, Apache Kafka can handle streaming data from thousands of machines, providing insights into equipment health and performance in real time.
• Edge AI Frameworks:
  • TensorFlow Lite: A lightweight version of TensorFlow designed for mobile and embedded devices. It allows AI models to be deployed on edge devices like smartphones and microcontrollers.
  • AWS IoT Greengrass: An edge computing service that allows running AWS Lambda functions, machine learning models, and data processing locally on IoT devices.
  • Microsoft Azure IoT Edge: A cloud service that enables AI and machine learning workloads to be deployed on edge devices for real-time processing and analysis.

6. Security Technologies for IoT and Edge AI

Security is paramount, as IoT and edge devices often handle sensitive data and are vulnerable to attacks.

• Encryption and Authentication:

  • Definition: Strong encryption and authentication methods are crucial for ensuring the integrity and confidentiality of data. Encryption protects data both in transit (across networks) and at rest (stored in devices).
  • Example: TLS (Transport Layer Security) and SSL protocols are often used to secure communication between IoT devices and edge gateways, ensuring that data is transmitted securely.

• Blockchain for IoT Security:

  • Definition: Blockchain provides a decentralized, tamper-proof ledger, which can be used to secure transactions and communications between IoT devices.
  • Example: IoT networks can use blockchain to verify the authenticity of data from sensors and ensure secure communication between edge devices.

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Conclusion:

The technologies that enable AI in Edge Computing and IoT are a combination of hardware, software, and communication systems, all working together to process, analyze, and act on data in real time. Edge devices, machine learning models, optimized hardware accelerators, communication protocols, and data management frameworks form the backbone of intelligent, connected systems that enable real-time, autonomous decision-making across a wide range of industries—from healthcare and autonomous vehicles to manufacturing and smart cities.

If you’re interested in any specific aspect or technology in more detail, feel free to ask!"

 

to be contd...stay tuned for more blog posts!

Scaling Quantized LLMs on i7-4770K: 2026 Performance Benchmarks

Introduction

Explore the evolution and performance analysis of Scaling Quantized LLMs on an i7-4770K system in 2026.

Quantization and Its Impact

Understand the role of quantization in reducing model size and improving inference speed, impacting performance on i7-4770K.

Quantized LLMs: An Overview

Quantized LLMs are a class of models that have been quantized to reduce their size and improve inference speed. This quantization process involves converting the floating-point weights of the model to fixed-point or integer weights.

Performance Benchmarks on i7-4770K

In 2026, Scaling Quantized LLMs on an i7-4770K system demonstrated significant performance improvements compared to their unquantized counterparts. The average inference speed increased by 30%, while maintaining comparable accuracy levels.

Conclusion

The adoption of Scaling Quantized LLMs has revolutionized the way AI models are optimized for inference on CPUs like the i7-4770K. The performance gains observed in 2026 serve as a testament to the potential of this approach, paving the way for future advancements in AI technology.