Artificial Intelligence Knowledge Library

Featured Articles

AI/ML - 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.

Read Article

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.

Read Article

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.

Read Article

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.

Read Article

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.

Read Article

Related Articles

Placing Greater Intelligence at the Edge

By having the intelligence closer to where the action is, it will be possible for systems to react more quickly to changing situations, for instance, where there are serious cost implications or are safety-critical, as well as allowing for better user experience without any annoying lags. Having access to edge-based ML inferencing will be beneficial in a broad array of application scenarios. Among these will be factory automation, machine monitoring, predictive maintenance, object recognition/categorisation, access control, and smart homes/buildings. 

Replacing centralised intelligence, to a certain degree, with a distributed strategy that puts more autonomy at the edge will call for locally situated processing capabilities. This is why microcontroller units (MCUs) are now starting to emerge that can provide the strong ML inferencing performance needed, while at the same time supporting low-power operation.

Read Article

How is AI Changing the Electronics Industry?

Artificial intelligence (AI) and machine learning (ML) have created an inflection point in product design. For two decades we have seen an exponential increase in data creation. The Internet of Things (IoT) is a big part of that, but companies like Avnet have been creating their own data for much longer.

Now, AI and ML are providing a viable way to make more use of it.

Avnet Senior Vice President Rebeca Obregon and Chief Information Officer Max Chan provide some insights into how distributors need to think about AI. They also talk about how distribution will react to the increasing demand for AI and ML hardware and software solutions.

Read Article

Why AI Must Redefine Processor Architectures 

The demand for AI running on programmable platforms is a unifying application. Microprocessors and microcontrollers all share similarities, but the applications they target tend to vary. AI is different.

General purpose is essential to the industry. It offers economies of scale in manufacturing. Common architectures enable a common toolchain, which makes software development manageable. Similar is good, it creates competition and innovation. These are positives for customers in a global industry.

Read Article

Why AI and Embedded Design Share the Same DNA

In simple terms, running an ML model on an embedded system comes with all the same challenges doing clever things on constrained platforms has always had. The details, in this case the model, vary, but the basics are the same. Engineers need to select the right processing architecture, fit the application into the type and amount of memory available, and keep everything within a tight power budget.

The key difference here is the kind of processing needed. ML is math-intensive; in particular, multidimensional math. ML models are trained neural networks, which are basically multidimensional arrays, or tensors. Manipulating the data stored in tensors is fundamental to ML. Efficient tensor manipulation within the constraints of an embedded system is the challenge.

Read Article

The Impact of Using AI in 5G Networks

To deliver an increase in capacity and throughput with a reduction in latency, 5G networks operate at frequencies up to 71 GHz. Trees and other foliage are great for the environment but not so good for RF energy propagation, particularly mmWave frequencies. To compound things, the density of vegetation and the level of attenuation it presents, changes with the seasons. 

Artificial intelligence (AI) technologies can significantly improve performance and user experiences.

Read Article

Advancing quality control: The Evolution, Impact and Future of Defect Visual Inspection (DVI) in Manufacturing

In the complex realm of manufacturing, Defect Visual Inspection (DVI) has emerged as a critical process, evolving significantly over time. The landscape of visual inspection has transformed from manual checks performed by artisans to AI-enhanced automated systems. As manufacturing processes continue to become more sophisticated, the importance and effectiveness of advanced Defect Visual Inspection (DVI) solutions escalate, presenting opportunities for improved quality control.

Read Article

Read all Artificial Intelligence Articles