Artificial Intelligence (AI) represents a groundbreaking opportunity in the field of electronics. By enhancing the performance of virtually any application, AI can serve as a key differentiator for products across diverse industries. As the world increasingly relies on intelligent machines, AI is set to play a pivotal role. People will turn to AI not only for basic information and complex analyses but also as tools that augment human creativity.
Despite its immense potential, AI is a complex and rapidly evolving engineering discipline, which can make mastering it a daunting task. Partnering with an experienced collaborator who understands the intricacies of selecting appropriate data sets, tools, software, and hardware components can significantly reduce development time and mitigate risks.
Effective AI implementation requires a balanced integration of hardware and software, along with the right machine learning (ML) algorithms. Avnet Silica brings extensive expertise in implementing machine learning on the edge, in the cloud, and on-premises. We support our customers in understanding and building their AI-based applications, focusing on:
- Computer Vision and Recognition Systems
- Generative AI, Generative AI at the Edge and Running LLMs Locally
- Machine learning (ML)
- Agentic AI
- Hardware for AI
Useful jump links:
Avnet Silica x DEEPX: Ultra-efficient, high-performance AI chips
Avnet Silica is now an exclusive DEEPX EMEA distributor, helping companies cut costs, save power, and bring intelligence to every device. Anywhere, anytime.
Innovation happens when the right technologies meet the right ecosystem. That’s why DEEPX and Avnet Silica are joining forces. To accelerate the adoption of edge AI across Europe.
DEEPX delivers ultra-efficient, high-performance AI semiconductors designed for on-device intelligence. Avnet Silica has demonstrated expertise in several areas within AI over the past decade, and also offers unmatched expertise in power management, embedded systems, and industrial IoT, complemented by a robust distribution network and comprehensive engineering support.
Focus Areas
Computer Vision & Recognition Systems
Enable machines to interpret and understand visual information from the real world, mimicking human vision capabilities. Computer Vision & Recognition Systems can identify and classify objects, detect anomalies, and facilitate advanced functionalities like facial recognition, autonomous navigation, and real-time surveillance.
Learn More
Generative AI & Generative AI at the Edge
Generative AI is the processing and preparation of information, mostly for human consumption. This includes everything from written content to the voice responses of smart assistants.
Generative AI at the edge is transforming the world around us by enabling low-latency, more secure and real-time AI on devices. Having AI 'on the edge' means personalised applications can run more efficiently without reliance on data centres.
Learn More
Machine Learning
Use Machine Learning to learn from data and improve system performance over time without being explicitly programmed. Analyse vast amounts of data to identify patterns, automate decision-making processes, and enhance various applications to improve system performance or predict hardware failures.
Learn More
Agentic AI
Artificial intelligence is entering a new phase with the rise of agentic AI – systems that move beyond reactive responses or content generation to act with autonomy and purpose. Whereas conventional AI executes predefined tasks or generates outputs on demand, agentic AI is designed to make context-aware decisions and continuously adapt to changing conditions without direct intervention. This shift unlocks applications where reliability, flexibility, and responsiveness are paramount.
Learn More
Hardware for AI
AI workloads, from edge inference to large-scale model training, place unique demands on electronic systems. AI often combines significant computation, high memory bandwidth, and continuous data flows, while adhering to strict latency and power constraints. Engineers are required to design systems that can process sensor inputs, execute models, and deliver insights reliably, all while maintaining energy efficiency and thermal stability.
Each tier of AI deployment, from sensor nodes and edge devices to near-edge infrastructure and centralised data, presents distinct hardware considerations. Optimising performance requires careful balancing of resources while considering integration and communication with other systems.
Learn More
AI/ML and Location, Location, Location
In the last few years, huge leaps in artificial intelligence (AI) and machine learning (ML) technology have enabled the incorporation of ‘intelligence’ in a rapidly growing number of products in applications as diverse as GPT tools, IC layout, and autonomous navigation. The adoption of AI and ML has made such products more powerful, faster, accurate and easier to use.
These examples demonstrate more than just the range of applications that can be aided or enabled by AI/ML. Each also gives a use case of where AI/ML can be deployed – in the cloud, on premise, or at the edge, respectively.
Learn More
AI and the Question of Processors
As transformative as artificial intelligence (AI) has already been in agriculture, medicine, finance, automotive, and elsewhere, there remain seemingly endless future benefits of AI. With no end in sight for the boom in the AI market, vendors of graphics processors (GPUs), general purpose processors (CPUs), neural processors (NPUs) and other specialized AI processors will be fighting each other for market share for years to come.
No single processor type is likely to emerge as a definitive winner, appropriate for all AI workloads, at least not in the foreseeable future. Each type of processor has advantages and drawbacks. As always in engineering, the tradeoffs will balance out differently for each use case. Avnet Silica can provide expert guidance as customers evaluate which processor type will be best for their specific AI workloads.
Learn More
Parsing AI - Glossary
A good first step toward exploring the adoption of AI technology is gaining familiarity with concepts and the specific engineering terminology associated with them. One of the difficulties with learning about AI is that some of the concepts are interdependent, and terms that aren’t strictly interchangeable can be used for each other. This can streamline verbal communications, but it can be confusing to the uninitiated. Compounding the confusion, any two organisations might have slightly different definitions for any given term.
Our purpose here is to be neither comprehensive nor fully definitive. It is to introduce the uninitiated to some of the most common concepts and terms in AI.
Learn More
Machines that Learn: A Deep Dive into AI/ML Models and Algorithms
Artificial intelligence (AI) and machine learning (ML) can be used to pull insights out of huge volumes of information quickly and efficiently. AI/ML can also give machines the ability to process information similar to the way humans do.
AI/ML can perform recognition and classification, predictive analytics, natural language understanding, and other tasks that are difficult or impossible to accomplish with traditional computing.
These capabilities lead to an impressive variety of use cases: voice recognition, autonomous driving, epidemiology, pharmaceutical design, software coding, financial trading, and more.
Learn More
The Evolution of Generative AI
Generative AI is not new. It has, however, recently taken an enormous leap in what it can do, inspiring no little amazement, as well as a bit of alarm.
A popular candidate as the first generative AI is the first chatbot, ELIZA, introduced in the 1960s, which was trained to respond like a psychologist. The subsequent history of AI research was marked by AI “winters” alternating with periods of resurgence typically coincident with some applicable technological advance, such as the introduction of microprocessors or the formulation of AI techniques such as back-propagation.
Learn More
AI's Edge: Fueled by Power Electronics
With its ability to substantially increase business efficiency, AI is poised — and has already begun — to have the same magnitude of impact as the arrival of the internet in the late 1990s. However, since something as simple as a generative AI query will use almost ten times as much electricity as a search engine query, the rapid growth of AI use is expected to drive a 160% increase in data centre power demand by 2030. By some estimates, data centres already consume up to 3% of the world’s electricity, so there is enormous pressure on operators to reduce energy consumption and increase the use of renewables. Designers of power supplies at all levels are therefore facing unprecedented demands for efficiencies and power densities and are increasingly dependent upon innovations in power electronics technology. As a leading supplier of power solutions, backed by a network of specialist partners and an in-house team of experts, Avnet Silica is the ideal partner to help solve the power challenges presented by the rising AI tide.
Featured AI Podcasts
Episode 77: Five Eyes, Zero Cloud: Vision AI From - Crop Fields to Factory Floors
Artificial intelligence is moving to the edge - and it's changing how factories operate, farms grow crops, and robots navigate the world. In this episode, Monica Houston from Tria Technologies walks us through Tria's Vision AI Kit QCS6490, an industrial-grade edge computing board that processes five camera feeds simultaneously, runs inference locally, and handles demanding tasks like image segmentation - all without needing cloud connectivity or even a cooling fan.
Monica discusses real-world deployments in agriculture (spot-treating crops to reduce pesticide use), factory robotics (autonomous mobile robots and robotic arms), and the practical challenges of moving AI from comfortable data centres to harsh industrial environments. We explore why latency matters, what happens when you can't rely on internet connections, and why power efficiency is the unsung hero of edge AI.
Episode 69: Focus on the Edge: How Videam’s Smart Cameras Solve Real-World Problems
In this episode, we explore the future of computer vision with Tommaso Scuccato and Marco Bergamin from Videam, an Italian startup that brings AI-powered vision systems to the edge.
Episode 60: The Road to AI-Enablement - How to Leverage Data for Business Success
What does it take for a company to become data-driven? Today’s discussion will explore Siemens’ strategies behind integrating AI and machine learning. Our guest is Michael Taylor, the Chief Data Scientist at Siemens. He is at the forefront of leveraging data to drive real business value. From smart infrastructure to predictive maintenance, Siemens is a prime example of how a company can be truly data-powered and AI-enabled.
Episode 59: Exploring Future Technology Trends - A Look Ahead with Avnet Silica
With the electronica fair just around the corner, we’re bringing you an exclusive discussion with two industry leaders from Avnet Silica. Today, I'm joined by Thomas Foj, Senior Director of Supplier Management, Solutions & Markets, and Strategy EMEA, and Michaël Uyttersprot, Market Segment Manager for AI/ML/Vision.
Episode 51: Smart Tech Synergy: Unveiling the Future of IoT and AI
We now live in a world where every device communicates, and every data point tells a story. And 2023 definitely will be remembered as the year of Artificial intelligence. Joining us today is David Ly, CEO of Iveda. They provide global solutions for cloud-based video AI search and surveillance technologies. We'll explore how integrating IoT and AI can revolutionize everything from smart cities to telehealth and advanced security systems.
Episode 50: Demystifying AI in IoT: Real-Time Analytics and Beyond
Together with Mohammed Dogar (VP, Global Business Development and Ecosystem), we'll explore the AI Center of Excellence at Renesas, unravel some common misconceptions about AI in the IoT space, and discuss the opportunities in real-time analytics.
Episode 45: Emotion in the Machine: Exploring Affective Computing
Today’s special guest is Nina Holzer. She is a researcher from the renowned Fraunhofer Institute for Integrated Circuits IIS and the team lead for 'Multimodal Human Sensing'. Together, we'll explore the fascinating intersection of human emotions, artificial intelligence, and the future of human-computer interaction. So, tune in as we delve deep into the heartbeats of our machines.
Featured AI/ML Products & Solutions
See the AI Knowledge Library
Head over to the AI Knowledge Library to see all of our AI and ML resources in one place. Explore articles, webinars, podcasts and more.
Need AI/ML support?Whether you have an AI/ML question for our team, or you're interested in one of our AI/ML solutions, you can get in touch with our experts here. |
Webinars
On-Demand AI Webinars
Get instant access to a huge library of AI webinars via our DEEP (Digital Event Experience Portal).
Technology
Power - Maximising Performance and Efficiency
AI models and applications require power solutions that are efficient and can maximise performance. Avnet Silica’s experts will help you match technologies aligned with your requirements to deliver the right solution without exceeding your budget
Vision AI
Avnet Silica & Tria QCS6490 Vision-AI Development Kit
The QCS6490 Vision-AI Development Kit features an energy-efficient, multi-camera, SMARC 2.1.1 compute module, based on the Qualcomm QCS6490 SoC device.
Webinar
Integrating AI in Smart Buildings
In this exclusive Avnet Silica webinar, experts from Avnet Silica, STMicroelectronics, Micron, Safesquare, Nordic Semiconductor, Tria Technologies, and MPS will share how AI-powered solutions are shaping the next generation of smart buildings.
AMD
Versal AI Core & Edge Series
Discover the AI Core Series and AI Edge Series, highly integrated, multicore compute platforms that can adapt to evolving and diverse algorithms.
NXP
i.MX95
The i.MX 95 applications processor family delivers safe, secure, power efficient edge computing for use in aerospace, automotive edge, commercial IoT, industrial, medical, and network platforms.
Avnet & Tria
QCS6490 Vision-AI Dev Kit
The QCS6490 Vision-AI Dev Kit is a new Tria dev kit. Based on a Qualcomm module, it is a complete solution for energy-efficient, multi-camera, vision applications that feature AI.
NXP
MCX-N Series
The MCX N94x and MCX N54x are based on dual high-performance Arm® Cortex®-M33 cores running up to 150 MHz, with 2MB of Flash with optional full ECC RAM, a DSP co-processor and an integrated proprietary Neural Processing Unit (NPU).
Renesas
Renesas RZ V2L
The RZ/V2L high-end AI MPU integrates Renesas' proprietary dynamically reconfigurable processor AI accelerator (DRP-AI), with Arm® Cortex®-A55 Linux processors, and dual Cortex®-R8 real-time processors.
DEEPX
DX-M1 M.2 LPDD5Rx2
The DEEPX DX-M1 M.2 module brings server-grade AI inference directly to edge devices. Delivering 25 TOPS of performance at just 2-5W, the module achieves 20x better performance efficiency (FPS/W) than GPGPUs while maintaining GPU-level AI accuracy.
STMicroelectronics
STM32MP2
ST’s STM32MP2 series microprocessors are designed to be industrial-grade 64-bit solutions for secure Industry 4.0 and advanced edge computing applications that require high-end multimedia capabilities.
Who is Avnet Silica's AI expert, Michaël Uyttersprot?
Michaël Uyttersprot is Avnet Silica's Market Segment Manager for Artificial Intelligence, Machine Learning and Vision. He has 20 years of experience in the industry, starting his career as an engineer in robotics. His current focus is on supporting the development and promotion of embedded vision and deep learning solutions to customers for projects involving AI and machine learning. Michael is seen as a thought leader in the field of AI and ML and has presented his work and thoughts to large audiences at industry events such as Hardware Pioneers Max. He also regularly joins webinars in partnership with major AI players in the semiconductor space, including NXP, STMicroelectronics and AMD.
Articles by Michael
- Revolutionising chatbot interactions
- Generative AI in Hospitality
- Transportation with Generative AI at the Edge
- AI Chatbots in Industrial Automation
- Enhancing Embedded Systems with Generative AI and Local LLMs
Podcasts featuring Michael
Episode 75: Billion-parameter brains in pocket-sized chips: The local AI revolution
Episode 59: Exploring Future Technology Trends - A Look Ahead with Avnet Silica
Episode 44: Seeing Beyond the Surface: AI in Defect Visual Inspection
Frequently asked AI questions
| Questions | Answers |
|---|---|
| What is artificial intelligence? | Artificial Intelligence (AI) is the capability of machines or computer programs to perform tasks that typically require human intelligence, such as perception, reasoning, learning, and decision-making. AI enables machines to mimic human behaviour and extract meaningful information from data, allowing them to sense, perceive, act, and adapt in various environments. |
| What is the difference between artificial intelligence and machine learning? |
|
|
What are some of the key areas or hot topics within artificial intelligence? |
|
| What is 'generative AI'? | 'Generative AI' refers to AI models that can create new content, such as text, images, music, or code, by learning from existing data and generating novel outputs. Examples include large language models (LLMs) and image generators. |
| What does the term 'AI at the Edge' mean? | 'AI at the Edge' means running AI algorithms directly on local devices (such as sensors, cameras, or embedded systems) rather than relying on cloud-based processing. This approach offers benefits like lower latency, improved privacy, reduced bandwidth usage, and real-time decision-making. |
| So what is 'Generative AI at the Edge'? | 'Generative AI at the Edge' refers to running models like LLMs (Large Language Models) or diffusion models directly on embedded or local devices, rather than relying on cloud servers. This enables real-time responses, enhanced privacy, and offline operation, but requires careful optimisation due to limited compute and memory resources compared to the cloud. |
| What is an 'LLM'? |
An LLM stands for Large Language Model. It is a type of artificial intelligence model designed to understand, generate, and manipulate human language. LLMs are trained on vast amounts of text data and use deep learning techniques (especially transformer architectures) to perform tasks such as:
LLMs are at the core of many modern AI applications, including generative AI tools like ChatGPT, Copilot, and others. They are called "large" because of the massive number of parameters (often billions or even trillions) and the scale of data used for training, which enables them to capture complex patterns and nuances in language. Running LLMs locally is increasingly possible thanks to advancements in model optimisation and hardware. Tools like Ollama allow users to run LLMs directly on their own machines, providing private, offline access to models such as Llama, Mistral, Gemma, and others. This approach is ideal for scenarios where data privacy, offline capability, or reduced cloud dependency are important. Running LLMs locally typically involves using quantised models (smaller, optimised versions) to fit within the resource constraints of local hardware, such as limited CPU, RAM, and storage. After downloading the model, no internet connection is required, and users benefit from fast startup and low-latency responses. |
| What is Agentic AI? | Agentic AI refers to AI systems that act as autonomous agents, capable of making decisions, taking actions, and adapting to their environment to achieve specific goals. These systems can operate independently, interact with other agents or humans, and learn from their experiences. |
| Why is hardware important in AI? |
Hardware is crucial for AI because:
|
| What are the main use cases for AI? How could it be used in the future? |
Current Use Cases:
Future Potential:
|
| What is Avnet Silica's involvement with AI? |
Avnet Silica is deeply engaged in the AI ecosystem, supporting customers with:
|
| Who are Avnet Silica's key AI suppliers and partners? |
Avnet Silica collaborates with a broad range of leading suppliers and partners in the AI and embedded vision space. Some of our most prominent AI suppliers and partners as of early 2026 include:
|
| Where can I find resources on artificial intelligence and all of its subtopics? |
Head over to our artificial intelligence knowledge library, which contains all of our AI resources, including technical articles, podcasts, webinars and much more! Want to familiarise yourself with AI terminology? See our comprehensive AI glossary. |
Tools
Design Hub
Browse and review over a thousand proven reference designs to accelerate your design process. Try our design tool and then export it to your CAD tool of choice.
Training & Events
Learning for better, faster projects builds
Connect with the Avnet Silica experts who will guide you to reach further with your projects with on-going seminars, workshops, trade shows and online training.
Market
Smart City
Smart City is an opportunity to reimagine our cities with the goal to change our quality of life and to supply clean water and sufficient energy for all citizens in an environmentally sustainable and equitable way. Let's make our cities smart.
Contact us
Have a question?
Int. Freecall - 00800 412 412 11 | Product or shop-related inquiries: OnlineSupportEU@avnet.com | For anything else, head over to our contact form.