How to Select an AI Chip for a New Hardware Product
Choosing an AI chip is one of the highest-leverage decisions in hardware development. Pick too little compute and the product cannot meet its promise; pick too much and you burn budget, power and battery life for nothing. Rather than reaching for a generic chip ranking, the right method is to start from concrete product requirements and work backward to a compute tier, an operating system and a vendor ecosystem you can trust for years.
Start With the Use Case, Not the Spec Sheet
Different products impose very different constraints. A sensible selection process begins by defining what the device must actually do:
- Camera products: continuous image streaming, object detection and privacy-sensitive on-device inference, where latency and power matter more than raw TOPS.
- Voice devices: always-on wake-word detection and speech recognition, favouring low leakage current, audio DSP and small models.
- Wearables: tiny batteries and tight thermals, where MCU-class TinyML often beats a power-hungry SoC.
- Industrial inspection: high-resolution input, deterministic timing and long service life, where lifecycle and supply stability can outweigh peak performance.
The Main Compute Tiers
Most AI hardware lands in one of three tiers. The table below compares them at a practical level:
| Tier | Typical Role | Strengths | Trade-offs |
|---|---|---|---|
| MCU + TinyML | Always-on, low-power sensors | Ultra-low power, cheap, fast boot, real-time | Limited memory, small models only |
| MPU / SoC with NPU | Cameras, voice hubs, gateways | Linux, rich software, higher TOPS, camera ISPs | Higher power, thermal and BOM cost |
| Standalone AI accelerator | Adding inference to an existing host | Scalable compute, flexible upgrades | Extra cost, integration and memory bandwidth challenges |
Linux vs RTOS: The Software Split
The choice of compute tier usually decides the operating system. RTOS suits MCU-class designs that need deterministic response and instant boot, while Linux is the natural home for NPU-equipped SoCs handling cameras, networking and richer application stacks. Mixing both is common: a small MCU handles real-time sensing and power management, while a Linux SoC runs the heavier model and connectivity.
Compute and Model Size
Match the chip to the model, not the other way around. Quantized mobile vision or keyword-spotting models run comfortably on an MCU, but detection or recognition networks with millions of parameters need an NPU-backed SoC or accelerator with adequate memory bandwidth. Estimating the model footprint early prevents painful rework later.
Memory, Power, Thermal and Cost
These four constraints interact. More compute drives higher memory, current draw and heat, which in turn forces larger batteries, heatsinks or fans, and higher cost. For battery products, average power in the always-on state usually matters more than peak performance. Always evaluate the total system cost, not just the chip price.
Chip Lifecycle and Supply Stability
A hardware product may ship for five to ten years. Confirm the vendor's longevity commitment, second-source options and roadmap before committing. A slightly slower chip with a decade of guaranteed supply is often the safer business decision than a faster part that disappears in two years.
SDK, Model Conversion and Development Ecosystem
The best silicon is useless without tooling. Check that the vendor provides a mature SDK, well-supported model conversion from frameworks such as TensorFlow Lite or ONNX, reference designs and an active developer community. A smooth path from training to deployment saves months of engineering time.
Matching Chips to Specific Products
| Product | Likely Direction |
|---|---|
| Smart camera | MPU/SoC with NPU and camera ISP, running Linux |
| Voice device | Audio MCU with TinyML, optional Linux gateway |
| Wearable | Ultra-low-power MCU plus TinyML |
| Industrial inspection | SoC with NPU or accelerator, long-lifecycle parts, Linux |
How We Help
From chip selection and model optimization to custom PCBA, thermal and structural design and certification support, we help teams turn an AI concept into a manufacturable, long-lifecycle product. Tell us your use case, model size and production volume, and our engineering team will help you choose the right compute tier and supply strategy.
Contact us to discuss your AI hardware requirements and get a tailored recommendation.
