Edge artificial intelligence (AI) technology is proving transformational for industrial enterprises as they shift focus toward the human- and planet-centric ideals of Industry 5.0. These solutions merge the power of edge computing with AI advancements to enhance efficiency, agility, and security through automation. They also reduce industrial reliance on centralized servers, helping to control both latency and costs. Altogether, edge AI helps teams strive to achieve the goals of Industry 5.0, reducing the strain on workers and supporting growth while respecting the limits of the planet. The use of these solutions is set to usher in the next generation of human-machine interfaces (HMIs), industrial robots, video monitoring, and more.
While edge deployments hold enormous potential to transform industrial environments, the journey to creating fully edge-powered facilities will require some creative problem-solving. Building devices capable of supporting AI at the edge and at an industrial scale will demand that developers push ingenuity and technology to their limits, all while facing pressure to overcome an age-old obstacle: space constraints.
Finding balance
The pursuit of power in small packages has been a key driver of technological advancement over the past few decades. Throughout the digital revolution, developers and designers have focused on finding ways to fit comparable computing capabilities into ever-shrinking footprints. Industrial Internet of Things (IIoT) devices have not been exempted from these efforts, and they’ve overcome significant spatial and computational challenges to get to their current operational state.
To work at the edge, IIoT devices must handle complex and mission-critical tasks in diverse—and sometimes extreme—environments, often with less-than-reliable access to external power. Further, these distributed systems tend to be “always on” and require continuous, real-time communication. Add in the challenge of managing thermal constraints, and the task of building AI-capable IIoT devices only becomes more complex.
While traditional AI models have revolutionized operations for many businesses, their computational demands can make them impractical for most edge deployments. Matching the broad potential of large AI models with the specialized operations of IIoT devices is a difficult hurdle to surpass. To enable AI at the edge, developers are starting to turn to smaller, task-specific AI models that are optimized for constrained environments. These models are trained on narrow datasets or compressed using techniques like pruning, quantization, or distillation. Though they perform a smaller set of tasks, they execute those functions more consistently and efficiently than traditional State-of-the-Art (SOTA) models.
Of course, as needs change, expectations grow, and edge AI devices become more sophisticated, compression alone is unlikely to ensure reliable and optimal performance. To deliver AI-ready solutions for edge ecosystems, developers will need to step back and consider how hardware and software work together to best balance space, performance, and efficiency.
Optimizing from both sides
There will always be a desire for systems to do more with the same (or fewer) resources, no matter how much tailoring, refining, and optimizing engineers accomplish. To this end, taking a multipronged approach to designing edge AI devices is critical to delivering the results that industrial organizations expect.
Ultimately, engineers will need to reconsider their approach to selecting the underlying hardware that powers their devices. When choosing components for edge AI builds, they must find options that prioritize:
- Latency reduction: speed is a primary motivator for industrial organizations investing in edge deployments, as these systems tend to be lower latency than cloud-based options. When processing and analytics happen at the edge—closer to where data is collected—the system doesn’t have to waste time on transfers. To accommodate changing needs and make real-time response more accessible and reliable, engineers may also want to prioritize equipment with parallel processing capabilities to ensure speed and reliability as computing demands rise.
- Energy efficiency: IIoT devices often have little space to spare for batteries or power conversion components, making low-power hardware advantageous for designers. However, there is a bigger picture to consider when it comes to efficiency, as concerns about AI and cloud computing’s energy consumption continue to grow. Increasing data volumes mean higher energy spend and carbon waste—not to mention that on-premises and/or cloud systems add ongoing operational costs. Using low-power components can help ensure that small-footprint devices work as intended, offering a more affordable alternative as cooling costs rise; many even support fanless computing. Even better, engineers may opt to use hardware that supports dynamic power management (e.g., sleep/wake modes) to further mitigate rising power demands.
- Secure operations: enhanced security is also an attractive feature of industrial edge AI systems. All industries are facing a growing risk landscape, but the cyber-physical threat to critical infrastructure and heavy industrial environments is particularly severe. On-device processing is a compelling option for many, as it limits data exposure during transmission to or from cloud environments. However, compact models may sacrifice traditional, software-level security protocols. As developers build devices, they will need to prioritize hardware-level protections such as encryption, secure boot, and neural network (NN) model authentication to fill gaps without compromising performance or efficiency.
- Adaptability: IIoT systems are long-term investments, and organizations need technology deployments that can change over time alongside their businesses. Opting for reprogrammable, over-the-air (OTA-)ready and/or NN-ready equipment can help mitigate downtime when upgrades or changes to the system are needed, making devices that offer these features attractive to organizations. General-purpose equipment chips can also be a good option for IIoT device developers, as they can handle a wide range of high-performance computing tasks to support flexibility in the design phase.
Accounting for all of the above is a significant challenge, and engineers have a range of options to consider—each with their own combination of strengths and limitations:
| Adaptability | Scalability | Performance | Efficiency | Dev. timeline | Security | |
| ASICs | Low | Low | High | High | Long | Strong |
| CPUs | High | Moderate | Moderate | Moderate | Short | Moderate |
| GPUs | Moderate | Moderate | High | Moderate | Short | Limited |
| FPGAs | High | High | Moderate | High | N/A | Strong |
| MCUs | Low | Low | Low | Very High | Short | Limited |
| NPUs | Low/Moderate | High | High | High | Medium | Limited |
| SoCs | Moderate | High | Moderate/High | High | Long | Strong |
Table 1. Comparison of computer processors and capabilities
While ASICs deliver exceptional performance and efficiency, they lack flexibility, making them ideal for fixed, high-volume applications rather than evolving industrial environments. Similarly, CPUs and GPUs offer fast development timelines and broad compatibility but often struggle with power and thermal constraints. MCUs excel in ultra-low-power scenarios but fall short in compute-intensive AI tasks. And while NPUs and SoCs offer stronger performance for AI workloads, they often require longer development cycles and are less adaptable.
Amongst their peers, Field Programmable Gate Arrays (FPGAs) stand out as the semiconductors most uniquely suited to the needs of complex, mission-critical devices. They combine high adaptability, strong security, and efficient deterministic performance in a reprogrammable architecture, allowing engineers to update NN models or optimize hardware without replacing the device. This flexibility makes them particularly valuable for mission-critical edge AI systems where requirements evolve over time and downtime must be minimized. While they may introduce some complexity into the build process, these specialized chips—and others like them—hold strong potential for industrial applications.
In high-risk environments, where seconds can make all the difference in averting an accident or malfunction, FPGAs have proven more than capable of rising to the task—once developers learn how to work with them. They’re already powering some of AI’s most sought-after applications: from computer-vision-supported safety monitoring to dynamic power management, FPGA-based builds help maintain operations even when implemented within small and power-strapped edge devices.
Within reach
As industrial facilities continue to march toward the human-centered, sustainably oriented promises of Industry 5.0, thoughtful integration of both hardware and software will be the key to unlocking that vision. Significant progress has already been made in overcoming space and power limitations, enabling applications that were once out of reach. But there is always room to improve.
Moving forward, success will depend on a holistic approach that brings together the right tools, technologies, and design strategies to ensure edge AI systems are efficient, adaptable, and aligned with long-term industrial needs.
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