Hardware, Software, Algorithms Edge AI: Competencies Companies Need to Build Now

Related Vendors

FAULHABER Drive Systems

Taiwan Machine Tool & Accessory Builders' Association (TMBA)

The use of AI in embedded systems is also becoming increasingly concentrated in the devices themselves. This opens up new possibilities, but also requires new knowledge: Only those who think hardware, software and application together can exploit the potential in practice.

As in many areas of technology, we are currently experiencing a fundamental turning point in electronics. Artificial intelligence has evolved from a primarily cloud-based approach to decentralized architectures. Edge AI in particular - i.e. the processing of AI algorithms directly at the point of data collection - is becoming increasingly important.

This development inevitably leads to a rapidly growing need for information and an increasing demand for specialized training courses.

Edge AI: More Than Just Algorithms or Hardware

A common misconception is that Edge AI is primarily a software issue. In reality, however, the key to successful use lies in the close integration of software and hardware.

Modern edge AI applications are often based on reconfigurable platforms such as field programmable gate arrays (FPGAs), which enable flexible and energy-efficient implementation of AI inference. The crucial point:

• Pure knowledge of AI algorithms is not enough; but
• Nor is isolated FPGA know-how sufficient

Only the combination of both disciplines - i.e. a deep understanding of neural networks, data flows and optimization techniques as well as their hardware-efficient implementation - enables powerful and economical edge AI solutions.

Challenge: Making Sensible Use of the Diversity of Technologies

Almost all major FPGA manufacturers have recognized the strategic importance of Edge AI and now offer comprehensive solutions, tools and reference designs. These include companies such as AMD, Altera, Efinix, Lattice Semiconductor and Microchip Technology.

These offerings range from optimized AI frameworks and hardware IP to complete reference platforms.

However, as with previous technological breakthroughs - such as the introduction of HDL synthesis or simulation - it is also evident here:

The mere availability of tools and reference designs does not guarantee successful use.

Challenges include:

• Selection of suitable architecture;
• Optimization of latency, energy consumption and resource requirements;
• Integration into existing systems; and
• Adaptation of AI models to hardware restrictions

FPGA Conference Europe: Platform for Innovation and Exchange

The growing importance of Edge AI is also evident at conferences. A key example is the FPGA Conference Europe, which is organized by PLC2 in cooperation with Elektronikpraxis.

Today, it is one of the leading specialist events for programmable logic in Europe and offers an important platform for:

• user-oriented solutions;
• technological innovations; and
• the exchange between developers, manufacturers and experts.

The four new special tracks "Embedded AI", which will be published for the first time in 2026, will specifically address the increasing relevance of edge AI. Renowned experts from the AI industry will present the current state of the technology, highlight innovative trends and provide concrete insights into realized applications in practical technical contributions so that you can benefit directly from best practices and future prospects.

Guidance to Accelerate your Programmable Solution

Featuring Special Tracks with Focus on Embedded AI

The FPGA Conference Europe – Europe's most important platform for manufacturer- and technology-independent, cross-application dialogue – provides embedded developers with orientation and practical assistance. Engage with industry leaders, gain insights from hands-on workshops, and connect with fellow experts eager to share the latest innovations and techniques. A new special track will focus on Embedded AI – intelligent systems that implement machine learning directly in programmable hardware.

Learn more

Wide Range of Applications

The areas of application for FPGA-based Edge AI are diverse and growing continuously. Typical fields of application include

• Industrial automation and manufacturing;
• Smart Home and Internet of Things;
• Automotive and Smart Mobility;
• Healthcare and medical technology;
• Security systems;
• Retail; and
• Applications in aerospace & defense.

In these areas in particular, low latency times, high energy efficiency and local data processing are decisive advantages - classic strengths of FPGA-based edge AI systems.

Edge AI is rapidly developing into a key technology in modern electronics. However, its successful use requires a rethink: away from isolated disciplines and towards an integrated approach of software, hardware and system understanding. This combination of expertise, practical experience and targeted training is therefore crucial right now in order to exploit the full potential of Edge AI and remain prepared for future developments. (sg)

*Eugen Krassin has been an FPGA trainer and founder of the PLC2 training center for 40 years.