• • • AI • • •
variety of algorithms. The scale and flexibility of these processors make them ideal for centralised computing, where energy, size and thermal constraints are less restrictive. But at the edge, requirements are very different.
Devices must respond in real-time while operating within strict limits on power, space and heat. This means that standard CPUs and GPUs are often too power-hungry or bulky to meet these demands efficiently.
Misaligned hardware can result in delayed responses, excessive energy consumption and potential security vulnerabilities, highlighting the need for processors specifically optimised for edge applications. ASICs are better matched to meet the constraints
of processing edge AI. This is because they are specifically designed for a particular application, combining sensor interfaces with lightweight AI processing in a single, optimised chip. Their architecture can include dedicated digital signal processing blocks, low-latency memory and optimised parallel computation paths to accelerate the precise calculations required, delivering fast, energy-efficient performance while keeping data local and secure. This design makes ASICs ideal for practical edge applications. A factory sensor can analyse vibration patterns on the spot to detect early signs of mechanical wear. A wearable device can process heart rate or oxygen levels in real-time and trigger instant alerts. Driver-assist systems in vehicles can interpret sensor data and react within milliseconds,
relying on the ASIC to execute the targeted algorithms required for each task. AI can only deliver reliable, timely and secure insights when its processing matches the right hardware and environment. Cloud CPUs and GPUs excel for large-scale, data-intensive tasks, while
ASICs provide purpose-built, efficient computation for real-time, local decision-making at the edge. However, it is in combining these strengths that organisations can truly unlock AI’s full potential.
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