AI Hardware – Solutions for Edge, Centralised, and Everything In Between
AI workloads, from edge inference to large-scale model training, place unique demands on electronic systems. AI often combines significant computation, high memory bandwidth, and continuous data flows, while adhering to strict latency and power constraints. Engineers are required to design systems that can process sensor inputs, execute models, and deliver insights reliably, all while maintaining energy efficiency and thermal stability.
Each tier of AI deployment, from sensor nodes and edge devices to near-edge infrastructure and centralised data, presents distinct hardware considerations. Optimising performance requires careful balancing of resources while considering integration and communication with other systems.
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The right hardware choices directly shape scalability, efficiency, and the viability of emerging use cases. Avnet Silica assists engineers in these critical decisions through its comprehensive expertise and a vast selection of innovative hardware, helping to drive the creation of AI systems throughout all application levels.
AI Hardware Considerations Across Deployment Tiers
Understanding the unique opportunities and hardware challenges across different deployment tiers is essential for designing impactful AI systems that meet the demands of modern applications.
- Feedback and Sensor Nodes: The functionality of AI hinges on data input and perception, and whether sensors are embedded in an edge AI device or operate as individual, distributed units connected to a larger system like those found in industrial settings or smart cities, core challenges remain: ensuring accuracy, maintaining data integrity and timing, and managing power consumption.
Engineers frequently need to integrate a wide range of components, like image sensors, accelerometers, LiDAR, and environmental sensors, and any captured information must be delivered reliably to the AI. This requires careful consideration of the entire data path, including conversion, filtering, connectors, and the selection of communication protocols that provide the necessary throughput and timing. Power also remains a critical factor, particularly for battery-powered or devices reliant on energy harvesting, where low operating consumption, duty-cycling, and wake sequencing are essential to extend operational lifetime.
Avnet Silica x DEEPX: Ultra-efficient, high-performance AI chips
Avnet Silica is now an exclusive DEEPX EMEA distributor, helping companies cut costs, save power, and bring intelligence to every device. Anywhere, anytime.
Innovation happens when the right technologies meet the right ecosystem. That’s why DEEPX and Avnet Silica are joining forces. To accelerate the adoption of edge AI across Europe.
DEEPX delivers ultra-efficient, high-performance AI semiconductors designed for on-device intelligence. Avnet Silica has demonstrated expertise in several areas within AI over the past decade, and also offers unmatched expertise in power management, embedded systems, and industrial IoT, complemented by a robust distribution network and comprehensive engineering support.
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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.
Memory & Storage
- Flash Memory & Embedded Storage: Non-volatile memory for storing trained models, datasets, and system firmware. Widely used in edge devices, sensor nodes, and embedded systems, flash and embedded memory retains AI workloads and configuration data even without power, enabling persistent operation and rapid deployment. Avnet Silica’s line card includes compact SPI Flash, eMMC modules, and other embedded storage formats suitable for a range of AI applications.
- SRAMs: Ultra-fast memory deployed as on-chip buffers or caches within AI accelerators, SoCs, and edge devices. SRAM supports low-latency processing for neural networks and continuous inference, ensuring real-time responsiveness for applications such as robotics and industrial automation.
- DRAMs: High-bandwidth, volatile DRAM is used in GPUs, TPUs, and edge/near-edge devices to store intermediate neural network data and support fast inference and training workloads.
- SSDs (NVMe, SATA): High-performance storage for near-edge and centralised AI systems. NVMe SSDs provide high-throughput, low-latency access for training datasets and captured data, while SATA-based SSDs offer reliable, cost-effective storage for industrial or enterprise applications.
Power & Thermal Management
- Wide Bandgap (SiC & GaN) Technologies: Silicon carbide (SiC) and gallium nitride (GaN) power semiconductors enable higher-efficiency power conversion, higher switching frequencies, and improved thermal performance compared with traditional silicon solutions. Avnet Silica’s power experts are on hand to support engineers in selecting and integrating both SiC and GaN technologies into their designs.
- Power Supplies: AC/DC and DC/DC power supplies deliver stable, reliable power across all AI deployment tiers. Pre-integrated power systems can help to accelerate development compared with designing discrete solutions, reducing engineering complexity and deployment time.
- Fan Motor Drivers & Heat Sinks: Effective thermal management is key to keeping AI hardware performing reliably. Avnet Silica provides a number of heat sink and fan driver solutions to ensure effective AI thermal management.
Networking & Interconnect
- Optical Transceivers: High-speed optical transceivers enable low-latency data transfer between AI compute nodes, ensuring large datasets and model parameters move efficiently across systems. Avnet Silica provides a range of solutions for leading manufacturers, ensuring reliable optical connectivity in centralised and near-edge AI deployments.
- Transmission & Switching: Switches and routers are crucial for handling the intricate data exchange among AI hardware, especially in larger edge or centralised systems, enabling fast communication with substantial bandwidth and minimal delay.
- Peripherals: Avnet Silica offers a wide range of peripheral and I/O interfaces that enable efficient capture and delivery of sensor information to AI systems.
Sensors & Data Acquisition
- Image Sensors & Cameras: AI intelligence depends on perception as well as processing power. Image sensors capture vital visual data for machine vision and feedback operations in robotics, industrial automation, and smart city applications.
- Motion & Position Sensors: Accelerometers, gyroscopes, and IMUs provide orientation and movement data for autonomous machines, wearables, and mobile robots. They are essential for intelligent localisation, navigation, and real-time control operations.
- Acoustic Components: Acoustic components capture audio signals for voice recognition, environmental monitoring, and acoustic AI models, supporting applications from smart assistants to industrial monitoring systems.
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Frequently asked AI Hardware questions
| Question | Answer |
|---|---|
| What is AI hardware and how does it differ from traditional computing hardware? | AI hardware refers to specialised chips and systems designed to accelerate artificial intelligence workloads, such as machine learning, deep learning, and neural networks. Unlike traditional CPUs, AI hardware includes GPUs, FPGAs, ASICs, and NPUs, which are optimised for parallel processing and high-throughput data operations required by AI algorithms. |
| What are the main types of AI hardware available today? |
The main types include:
You may find our 'FPGA vs GPU vs CPU vs MCU - hardware options for applications' article, written by our AI expert, Michael Uyttersprot, interesting. |
| What are the key considerations when choosing AI hardware for a project? |
Key factors include:
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| How is AI hardware used in physical AI applications (robots, autonomous vehicles, etc.)? | Physical AI leverages AI hardware to enable robots and machines to perform tasks in the real world, such as object recognition, navigation, manipulation, and decision-making. This includes applications in robotics, autonomous vehicles, industrial automation, and smart devices, where real-time processing and sensor fusion are critical. |
| How does memory architecture impact AI hardware performance? |
High-bandwidth memory (HBM) and on-chip memory are crucial for AI workloads, enabling faster data access and reducing bottlenecks. Solutions like HBM and large on-chip SRAM are increasingly adopted in AI accelerators to support large models and real-time inference. The need for high-bandwidth memory and advanced DRAM production that prioritises AI has caused a severe global memory shortage that is expected to last until at least 2027. |
| What is the role of software in AI hardware solutions? | AI hardware is only as effective as the software stack that supports it. Leading vendors provide comprehensive SDKs, APIs, and libraries (e.g., NVIDIA CUDA, Google TensorFlow) to enable developers to fully leverage the hardware’s capabilities for AI model development, optimisation, and deployment. |
| What are some real-world examples of physical AI applications? |
Physical AI is powering:
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| What is Avnet Silica's involvement with AI hardware? |
Avnet Silica is deeply engaged in the AI hardware ecosystem, acting as both a distributor and a solution enabler for AI and embedded vision applications. We support customers across the entire AI value chain, from providing off-the-shelf AI accelerators and development kits to enabling custom silicon design through our Avnet ASIC Solutions division. Avnet Silica's activities include:
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| What are some of Avnet Silica's key AI hardware partners? |
Avnet Silica's AI hardware portfolio is built on strong partnerships with both established industry leaders and innovative startups. Key partners include:
These partnerships ensure Avnet Silica can offer a comprehensive, best-in-class portfolio for AI hardware, from MCUs and FPGAs to dedicated AI accelerators and complete edge AI systems. |
| Where can I find resources on hardware for AI? | Head over to our artificial intelligence knowledge library, which contains all of our AI hardware resources, including technical articles, podcasts, webinars and much more! |
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