• • • AI • • •
ASICS MEET AI AT THE EDGE: HOW APPLICATION-SPECIFIC CHIPS SUPPORT FAST, EFFICIENT EDGE AI
Whether it’s helping doctors predict health outcomes, powering connected cars or optimising factory floors, AI is transforming the way industries work. But it can only deliver reliable, timely and secure insights if its processing location is matched to the right hardware. By Ross Turnball, Director, Swinson Silicon Systems
H
ere, Turnbull explores how the choice of processing location and purpose-built hardware determines AI’s effectiveness, and how ASICs support real-time intelligence at the edge.
AI adoption is accelerating across industries. According to Stanford University’s Human-Centred AI Index, 78 per cent of organisations had integrated AI into daily operations in 2024, a 23 per cent increase from the previous year. This growth is generating vast amounts of data from devices, sensors and systems, all of which must be processed efficiently to produce meaningful insights.
Effective processing is essential for AI to function as intended. Every algorithm, from simple threshold- based models to complex machine learning networks, requires hardware to execute calculations.
In the clouds or at the edge? AI can be processed in two main environments: the cloud or at the edge. Cloud computing, powered by high-performance CPUs and GPUs, excels at training and running large-scale models. Aggregating vast datasets from thousands of devices enables industries to uncover insights and coordinate AI across sites and regions. This makes the cloud indispensable for tasks that demand scale and computational depth, such as enterprise analytics, global logistics optimisation and research applications.
Edge AI, by contrast, is designed for small, latency-sensitive applications where immediate action is critical. Rather than sending raw data to the cloud, devices analyse information locally, reducing delay, easing network demands and keeping sensitive data on-device.
As edge AI is purpose-built and lightweight, it focuses on executing specific algorithms efficiently within the device’s own constraints. For example, a factory sensor might monitor vibrations to predict wear, while a wearable device could track vital signs in real-time. Driver-assist features in vehicles can also react within milliseconds.
The distinction between cloud and edge is not about superiority, but suitability. Cloud systems remain vital for scale, while edge devices support immediate, targeted decision-making. However, wherever AI runs, its performance depends on its hardware being well-matched to the task.
Processing intelligence In the cloud, general-purpose CPUs and GPUs excel because they can process vast datasets, support complex model training and run a wide
34 ELECTRICAL ENGINEERING • NOVEMBER 2025
electricalengineeringmagazine.co.uk
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