AI Hardware – Solutions for Edge, Centralised, and Everything In Between

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• Edge Devices: Edge devices bring AI inference closer to where data is generated, reducing latency and dependence on centralised systems. Their design challenge lies in matching the processing power of embedded MCUs, MPUs or SoCs to model complexity, while keeping energy use and thermal output within practical limits.

The diversity of today’s edge applications, from compact wearables and home devices requiring ultra-low-power operation to industrial and robotics platforms needing higher compute density, demands a broad spectrum of processing hardware, including innovative solutions specifically optimised for AI workloads. Connectivity is equally decisive, with wired and wireless options such as Wi-Fi, BLE, LPWAN, or 5G each offering distinct trade-offs in bandwidth, coverage, and determinism. Local memory and storage must also sustain AI processes without bottlenecks, while thermal management, through efficient layout, passive cooling, or lightweight active methods, is vital for maintaining system longevity.

• Near-Edge: Near-edge systems extend local processing capacity, providing a middle ground between individual edge devices and centralised data centres. These systems often orchestrate numerous processors or accelerators to support collaborative workloads such as traffic control, factory automation, or distributed robotics.

The hardware challenge is in maintaining predictable performance in higher complexity systems without exceeding power, cooling, or financial budgets. High-bandwidth interconnects and memory architectures provide low latency between devices, while redundancy and fault-tolerant design must be considered to ensure continuous operation in mission-critical environments. Near-edge infrastructure will also be the ideal foundation for emerging agentic AI applications, which enable continuous, autonomous reasoning across local nodes to enhance responsiveness and operational efficiency.

• Centralised Data centres: Centralised data centres provide the scale necessary for AI training, running neural networks and complex generative AI applications, and performing high-throughput inference. In this continuously evolving environment, engineers must ensure hardware meets the demands of increasing compute density, memory capacity, and interconnect bandwidth while maintaining energy efficiency and managing thermal loads.

Advanced power solutions, including wide bandgap semiconductors, are key to driving innovation, like supporting emerging rack architectures and higher system voltages. Thermal management relies on effective active cooling, fan motor drivers, heat exchangers, and liquid-cooling systems that must maintain consistent operating temperatures. Reliable data transmission depends on optical transceivers and high-speed interconnects to ensure efficient, low-latency communication between compute nodes. Fault-tolerance, hardware redundancy, and workload orchestration are also considerations for engineers looking to maintain reliable operation under intensive AI workloads.

Key AI Hardware Applications

Combining in-house technical AI expertise with a range of innovative technologies, Avnet Silica has the solutions engineers need to accelerate development, optimise system performance, and ensure reliable, efficient operation across diverse application tiers and workloads.

Compute & AI Accelerators

• MCUs and MPUs: Compact, energy-efficient processors that provide local computation for edge AI and sensor nodes. Many MCUs and MPUs now feature integrated AI accelerators or dedicated cores to support continuous inference and lightweight AI workloads. These devices are well-suited to industrial IoT, wearables, and smart sensors where power, size, and real-time performance are critical.
• SoCs and SoMs: Integrated platforms that combine connectivity, memory, and processing, often with dedicated AI cores. These devices support higher-performance edge inference and increased multimodal sensor fusion, bridging the gap between MCUs and larger infrastructure.
• FPGAs: Flexible, reconfigurable accelerators for deterministic latency and application-specific AI pipelines. FPGAs are ideal for near-edge inference or specialised robotics and industrial automation tasks, where customisation and real-time processing are essential.