Choosing the Right Edge Compute Platform for AI: GPU, NPU, MPU or FPGA?

Engineers developing AI-enabled edge applications face many challenges, not the least of which is coping with an increasingly complex hardware landscape. The type of processing hardware at the heart of the application has a direct impact on system performance, cost, security, maintainability and longevity.

Four hardware classes dominate edge AI discussions today: graphics processing units (GPUs), neural processing units (NPUs), micro processing units (MPUs) and field programmable gate arrays (FPGAs). Each provides distinct execution characteristics, including architectural efficiency, programmability, determinism and power consumption.

This Teck Talk provides a brief overview of the strengths and weaknesses and indicates some best-fit use cases for each class.

GPU

Graphics processing units remain the default choice for many AI workloads thanks to their mature tooling, broad operator support and high parallel throughput. Architecturally, a modern GPU has thousands of single-instruction, multiple data (SIMD) style cores that are ideal for matrix and tensor operations. This makes them exceptionally effective for convolutional neural networks (CNNs), transformers and other dense-compute workloads.

Strengths:

· Flexibility. GPUs support a wide range of model architectures without the need for hardware re-synthesis or custom kernels. This is useful for developers iterating quickly or supporting heterogeneous customer needs.

· Developer Ecosystem. CUDA, ROCm, TensorRT, ONNX Runtime and well-maintained SDKs lower friction for model deployment and optimisation.

· Performance Headroom. Even mid-tier mobile and embedded GPUs provide significant FLOPS for vision, speech and multimodal inference.

Weaknesses:

· Power. GPUs tend to draw more power than specialised accelerators so, in ruggedised or battery-constrained environments this may be prohibitive.

· Cost. For industrial deployments, the bill of materials (BOM) for GPU-equipped edge devices can be substantially higher.

· Longevity. Consumer-grade GPUs may face availability changes across product generations, complicating multi-year field support.

Best Fit Use Cases:

· Rapid model evolution or frequent over-the-air (OTA) updates.

· High-density inference in applications that are not power constrained.

· Applications requiring rich operator support – for example, multimodal pipelines that combine computer vision, neural language processing and machine learning.

NPU

Neural processing units have become increasingly common in mobile SoCs, automotive subsystems and dedicated edge inference modules. They are purpose-built for neural network operators, often using systolic-array compute blocks or other dataflow architectures to maximize efficiency.

Strengths:

· Power Efficiency. NPUs often deliver an order of magnitude better performance-per-watt than GPUs… for supported operators (see first weakness below).

· Cost Efficiency at Scale. Many NPUs are tightly integrated into SoCs, reducing component count and making them attractive for high-volume embedded products.

· Deterministic Latency. Their predictable dataflow is advantageous for real-time control loops, robotics and safety-critical inference.

Weaknesses:

· Operator Coverage. NPUs excel with mainstream layers but may lack support for emerging model architectures or exotic kernels.

· Framework Maturity Variability. Toolchains differ significantly across vendors. Also, conversion pipelines may require quantisation or operator fusing that affect model accuracy.

· Limited Reconfigurability. If the NPU cannot execute an operator efficiently, fallback to the host CPU/MPU, which will most likely introduce latency.

Best Fit Use Cases:

· Applications with relatively stable model architectures (i.e. unlikely to change drastically throughout the lifecycle).

· Power-sensitive scenarios such as drones, wearables or smart cameras.

· High-volume deployments where BOM optimisation matters.

MPU

Microprocessor units that are typically based on ARM, RISC-V or x86 architectures remain a pragmatic choice for many edge AI projects, specially where lightweight models, moderate throughput and flexible software ecosystems are expected.

Strengths:

· Simplicity and Maintainability. MPUs support full operating systems, robust OTA update infrastructure, containerisation and standard security hardening practices.

· BOM Efficiency. For many embedded products, an MPU with optimised INT8 inference libraries (e.g., ARM Compute Library, XNNPACK) can meet performance targets without dedicated accelerators.

· Software Flexibility. It is possible to deploy classical algorithms, business logic, security layers and AI inference on a single chip.

Weaknesses:

· Lower Throughput. MPUs cannot match the performance of GPUs, NPUs or FPGAs for heavy convolutional or transformer-based workloads.

· Thermal Headroom. Sustained inference loads can push some MPUs beyond intended thermal envelopes.

· Model Constraints. To achieve acceptable latency, models often require aggressive quantisation or architecture simplification.

Best Fit Use Cases:

· Traditional embedded applications augmented with lightweight neural inference.

· Highly maintainable systems requiring Linux-class capabilities.

· Cost-sensitive deployments with moderate performance needs.

FPGA

Field programmable gate arrays stand apart from the other three hardware classes discussed in this Tech Talk article. This is because they offer reconfigurable logic, customisable data paths and operator pipelines, all of which can be tuned for specific models or application semantics. Unlike fixed-function accelerators, FPGAs enable deep optimisation of high-speed, data movement.

Strengths:

· Long Product Lifecycles. FPGA families often remain in production for many years, critical if your company is serving industrial or defence sectors needing decade-scale availability.

· Determinism. Their pipeline-centric execution model supports consistent latency, ideal for safety systems, advanced sensor fusion and high-frequency inference tasks.

· Security. Bitstreams can be encrypted and authenticated, and some FPGAs offer secure enclaves, making them suitable for tamper-resistant deployments.

Weaknesses:

· Development Overhead. FPGA design requires hardware-aware development practices, whether via register transfer level (RTL), high level synthesis (HLS) or (multi-level intermediate representation (MLIR) based compilation. This introduces a steeper learning curve.

· Slower Iteration. Updating the hardware pipeline is more involved than releasing a new model for a GPU or MPU.

· Economy of Scale. For low-volume deployments, FPGAs can be cost-effective. For mass-market applications, ASIC-based NPUs may offer better unit economics.

Best Fit Use Cases:

· Long-life industrial or mission-critical systems.

· Scenarios requiring tightly bounded latency or specialised sensor fusion.

· Workloads benefiting from ultra-low-latency streaming pipelines.

Decisions, decisions…

Above we outlined the strengths and weaknesses of the four hardware classes most popular in edge AI; certainly for compute intensive applications. We also suggested a few best fit use cases for each. However, let’s turn things around and consider five key aspects of hardware selection. Which hardware classes are tick the boxes?

*Intergrated into a SoC

**Provided a robust OS is also used

Conclusion

Not surprisingly, no single compute architecture universally outperforms the others for edge AI. Instead, the optimal choice is heavily contextual, shaped by application constraints, lifecycle expectations and regulatory environment.

Hardware selection must be based on workload semantics, deployment scale, security and maintainability objectives. Doing so will maximise both technical performance and long-term competitiveness in the fast-growing landscape of edge-native AI solutions.

If you’re ready to turn edge AI potential into real-world performance, our Intelligent Solutions team at Simms is here to help. From selecting the right compute architecture (whether it’s GPU, NPU, MPU or FPGA) to delivering fully configured, ruggedised platforms tailored to your application, we can guide you at every step - including design, optimisation and lifecycle support. Explore our Intelligent Solutions portfolio to find the compute building blocks that accelerate your edge AI deployments and get in touch.