Selecting the right hardware platform lays out the foundation for performance, scalability, certification, and lifecycle cost. The wrong choice can lock a product into one ecosystem or delay its launch by months. 

In embedded AI, more computing power isn’t always better. True performance lies in performance per watt; that is how efficiently a device converts power into inference capability.

For fanless, battery-powered, or size-constrained systems, efficiency often determines product success. While TOPS (Tera Operations Per Second) is often cited as a measure of performance, it is a misleading metric without considering power. For embedded AI systems, performance-per-watt is the true measure of efficiency, balancing computing power against energy, heat, and size constraints. Modern platforms like Hailo or Lattice achieve exceptional efficiency through purpose-built architectures designed specifically for edge inference. 

Etteplan helps clients compare architectures based on performance-per-watt to ensure each design achieves the best possible balance between power, cost, and reliability. 

Every AI device has unique requirements for computing power, memory, connectivity, and environmental tolerance. When these are mismatched, even the most advanced algorithms can underperform. Early hardware decisions directly affect certification, scalability, and lifecycle costs. [Explore: Secure Edge AI – Building Trusted and Compliant Devices]

Key factors to consider include: 

Many teams enter AI development without a clear end goal. Marko notes that some companies choose platforms simply because they are popular like NVIDIA Jetson rather than because they fit the workload. This often results in vendor lock-in, overspending, or hardware that can’t scale. 

Another frequent issue is unclear requirements. Teams rush into prototyping before defining whether the device must perform real-time inference, operate offline, or meet safety-critical standards. 

Choosing the right hardware starts with understanding what each processor type can do for your application. Most real-world systems use combinations of these processors for instance, an MPU for general computing, a GPU for parallel workloads, and an NPU for neural inference acceleration. In many cases, these are integrated into a single System-on-Chip (SoC) for compactness and energy efficiency. 

Once the processor landscape is clear, the next step is to map each hardware type to your specific data and application needs. Not all AI workloads require dedicated accelerators. Ekkono and other lightweight models enable real-time inference for simple signals and even basic vision tasks on MCUs or SoCs. hardware acceleration is only required for more demanding, high-throughput, or low-latency tasks (e.g., multi-camera analytics, complex vision). 

These hardware tiers illustrate how different architectures scale across data complexity, energy budgets, and performance targets.  

Different industries demand different hardware performance levels: 

While the earlier table outlined common hardware tiers by application, this table provides a complementary view of data compatibility and processing capability.

While every project is unique, this matrix provides a high-level reference for matching workloads to hardware. As AI hardware evolves rapidly, these classifications will continue shifting making early feasibility testing essential. 

Etteplan works hands-on with NVIDIA Jetson for GPU acceleration, Intel OpenVINO for portable inference on CPUs, iGPUs, and VPUs, STM32 for ultra-low-power TinyML, Hailo NPUs for high-efficiency vision workloads, and Alif Semiconductor for battery-friendly always-on sensing. This breadth allows us to benchmark platforms on performance-per-watt, thermals, bill of materials (BOM) impact, toolchain maturity, and lifecycle support ensuring each architecture fits the product’s purpose rather than the trend. 

Etteplan supports customers in analyzing data types, computing demands, and certification paths to match the right architecture to each use case, ensuring designs that are scalable, compliant, and future-ready. 

Once the right hardware architecture has been identified, the next challenge is how to prototype and scale it efficiently. Moving from development boards to production-ready modules requires balancing speed, certification, and cost. The next section explores how System-on-Modules (SOMs) and custom hardware designs help product teams turn tested AI concepts into manufacturable solutions.

A practical path to implementation follows three stages: 

This staged approach minimizes rework, allowing teams to validate performance early and scale confidently. While the outlined path fits heavier Edge AI use cases, not every AI workload requires specialized hardware acceleration. Many practical edge applications such as anomaly detection, sensor fusion, or threshold-based monitoring perform efficiently on standard CPUs without dedicated GPUs or NPUs. 

Etteplan helps customers evaluate when simple CPU inference is enough, and when moving to accelerated hardware brings measurable value. 

Pre-certified modules shorten time-to-market but increase unit cost. Custom boards reduce long-term cost but extend validation cycles. Etteplan helps clients find the sweet spot between speed and scalability, advising when to invest in custom design and when to leverage off-the-shelf solutions.

Hardware selection is also linked to upcoming EU regulations like the Cyber Resilience Act and AI Act, which emphasize secure-by-design principles even at the hardware level. [Learn more about compliance under the EU AI Act – What Product Teams Must Do Now]

Etteplan bridges the gap between embedded hardware and artificial intelligence. We help customers validate hardware architectures through early prototyping, using general development boards to test algorithms before migrating to optimized, custom-built designs. 

From concept and feasibility to certified deployment and lifecycle management, our experts align each project with security, cost, and performance goals.  Etteplan acts as a technology-agnostic advisor, bringing broad experience across hardware families, ecosystems, and SDKs. “We have a wide understanding of the providers and AI systems available. We can propose pre-existing systems or suggest how customers should proceed,” says Marko. 

Etteplan’s support covers: 

Etteplan’s partnerships with NVIDIA, Intel, and STMicroelectronics and collaboration with software specialists like Ekkono will enable balanced solutions that merge AI performance, security, and compliance. 

Choosing hardware for AI is as much a business decision as it is an engineering one. This is a structured framework to help R&D teams make informed choices early in the process: 

Additional Evaluation Factors include:

This structured approach ensures that the chosen hardware aligns with both technical goals and business constraints. 

Hardware design is no longer a one-time effort. As AI capabilities evolve, products must remain adaptable and able to update firmware, upgrade modules, and integrate new accelerators without major redesigns. The maturity of the surrounding ecosystem whether SDKs (Software Development Kit), compilers, and development tools determines how fast and reliably an AI model can reach devices in production.   

Embedded AI is evolving beyond single-task inference toward more advanced capabilities. Emerging trends include multimodal processing, combining vision, audio, and text, and early experiments in lightweight generative AI for constrained devices. While these features will not appear together in every product, modular hardware and mature ecosystems will help manufacturers adopt them selectively as technology advances. [Read next: AI-Empowered Products – Turning Intelligence into Value]

Etteplan helps companies plan for evolution, not just launch. By combining embedded engineering, AI architecture, and regulatory expertise, we ensure devices stay secure, compliant, and high-performing throughout their lifecycle. 

The AI hardware landscape is rapidly advancing, with each ecosystem offering unique strengths: 

We evaluate each platform for SDK maturity, toolchain stability, and developer support. For example, NVIDIA JetPack, Intel OpenVINO, and STM32Cube AI offer complete toolchains for model optimization and deployment, while Hailo and Qualcomm lead in ultra-low-power AI acceleration. Selecting hardware with the right ecosystem shortens integration time, reduces engineering risk, and accelerates go-to-market success. 

Etteplan is helping clients prepare for this future by designing modular, upgradeable hardware architectures that adapt as AI workloads evolve. By combining embedded design, AI architecture, and lifecycle planning, Etteplan ensures that today’s devices are ready for tomorrow’s intelligence. 

The success of any Edge AI product depends on getting the hardware right balancing performance, cost, and compliance from day one. With Etteplan’s end-to-end expertise from requirement definition to certified deployment, companies can avoid costly redesigns, accelerate market entry, and build AI devices that last. 

Our AI hardware consulting team ensures each design is compliant, future-proof, and aligned with evolving industry standards.

Book a Hardware Selection Consultation with Etteplan to get a tailored comparison of AI platforms for your product.