Designing for the Real World: Hardware Constraints in Edge AI

Designing Edge AI systems is not as simple as porting cloud models to embedded devices, as edge systems must operate under significant restrictions.

Unlike cloud or data center environments, edge devices are deployed with tight power, environmental, and physical space constraints. They often need to run AI inference in real time while remaining low power, reliable, and secure even in harsh or remote conditions. Here is how we at NeuronicWorks can help clients overcome common hardware challenges in Edge AI systems:

1. Constraint: Limited Power Availability

Edge devices frequently operate on batteries and/or energy-harvesting systems, making power efficiency paramount.

Solution:

• Use ultra-low-power, dedicated ICs with integrated AI accelerators to minimize energy drawn during inference.
• Use ultra-low-power MCUs or SoCs with advanced deep-sleep capabilities.
• Implement event-driven wake-up strategies, where processors remain in a deep-sleep mode until a sensor detects relevant input (e.g., motion, sound, anomaly).
• Leverage dynamic voltage and frequency scaling (DVFS) to scale performance based on real-time workload.
• Adopt battery-friendly architectures such as Analog Devices MAX78002, ST micro STM32N6 family, NXP i.MX RT700, Infineon E84 family.

2. Constraint: Processing Power and Memory Footprint

AI models are often resource hungry, but edge devices must process them with limited CPU and memory.

Solution:

• Optimize models using quantization (e.g., int8 vs float32), pruning, and knowledge distillation to reduce the number of parameters and operations required.
• Offload parallel or repetitive workloads to on-chip DSPs or NPUs, freeing up general-purpose CPU cores.
• Use streamlined AI architectures like MobileNet, YOLOv4-tiny, or EfficientNet-Lite that are built for constrained environments.
• Employ memory-aware scheduling to allocate buffers only when needed and free resources proactively.

3. Constraint: Heat and Thermal Dissipation

Compact devices with high processing demands can quickly overheat, reducing reliability.

Solution:

• Select processors with integrated thermal management, or lower maximum sustained performance to reduce heat buildup.
• Use passive-cooled designs, incorporating features like aluminum enclosures as heatsinks, for sealed, fanless systems.
• Profile workloads early to understand thermal behavior and set thermal throttling thresholds before deployment.

4. Constraint: Form Factor and Space

Edge devices often need to be compact, whether they are wearables, drones, industrial sensors, or remote cameras.

Solution:

• Utilize multi-function system-on-chips (SoCs) to reduce component count (e.g. camera input, AI engine, security unit all in one).
• Design compact, space-optimized, multi-layer PCBs, utilizing rigid-flex and other technologies as needed.
• Eliminate unnecessary peripherals. Every square millimeter counts in miniaturized devices like wearables or drones.

5. Constraint: Inconsistent or No Connectivity

Edge devices cannot always rely on constant cloud connectivity.

Solution:

• Design edge devices to be capable of running autonomously, with local inference and decision making.
• Buffer non-critical data for batch uploads when connectivity resumes, reducing reliance on real-time cloud access.
• Use lightweight protocols (e.g. MQTTS, CoAP) and local mesh networking where multiple edge devices can relay data cooperatively.

6. Constraint: Device-Level Security Risks

AI systems often store sensitive data and models, which must be protected against tampering.

Solution:

• Use secure boot, hardware-backed root-of-trust architectures, and cryptographic accelerators to protect firmware, model and data integrity.
• Implement on-device encryption and secure key storage.
• Design for remote attestation and update verification to maintain security throughout the device lifecycle.

Overcoming hardware constraints is about making smart, system-level optimizations and trade-offs, and choosing technologies that are built for the edge — not repurposed from the cloud.

These considerations make architectural decisions and hardware selection critical.

With hardware constraints clearly in mind, choosing the right processor becomes one of the most impactful decisions in your Edge AI design journey.

Choosing the right processor is a balancing act between performance, power efficiency, and scalability. At NeuronicWorks, we often evaluate processors based on:

• Inference Performance: Measured in TOPS or FPS — this must align with your workload (e.g. image detection vs. audio classification).
• Power Budget: Especially important for wearables, portable devices, and outdoor sensors.
• Scalability: Can the same family of processors support low-end and high-end variants of your product?
• Toolchain Ecosystem: Availability of optimized models, SDKs, and pre-integrated AI frameworks can shorten development time.

Designing for Edge AI is about trade-offs. Power, performance, reliability, and security must all be addressed at the hardware level before a product can scale. By understanding the real-world constraints and selecting the right platform early, you lay the foundation for AI systems that work not just in theory, but in the field.

Beyond general-purpose MCUs, a new generation of dedicated Edge AI chips is emerging, built to deliver powerful inference within minuscule energy and space limits. These chips focus on doing one thing well, such as wake-word detection, vibration classification, or anomaly detection, often consuming only microwatts of power.

Examples of Specialized AI Chips

Blumind

• Analog neural network architecture (AMPL™) built on standard CMOS technology, achieving up to 1,000× lower power than digital equivalents.
• Ideal for always-on applications like camera, voice, or motion detection.

Applied Brain Research

• The TSP1 (Time Series Processor) specializes in real-time sensor data processing (audio, vibration, biosignals).
• Enables full speech recognition at < 50 mW and keyword detection at < 2 mW.

Aspinity

• AnalogML™ architecture (AML100/200) enables always-on signal processing at micro-watt levels.
• Used in automotive, industrial, and IoT devices for vibration, audio, and event detection.

These chips are best suited for “always listening” or “always sensing” devices where a low-power co-processor can pre-filter data and wake the main MCU only when necessary.

In many NeuronicWorks designs, we combine such specialized AI hardware with general-purpose processors to achieve a hybrid balance between ultra-efficiency and flexibility.

When flexibility and integration are more important than ultra-specialization, general-purpose MCUs with built-in AI acceleration strike the right balance. They allow designers to handle sensing, control, connectivity, and moderate AI workloads all within one compact, efficient platform.

STM32 Family (STMicroelectronics)

• The new STM32N6 series integrates a Neural-ART NPU with an Arm Cortex-M55 core and DSP.
• Delivers low-latency AI inference with full access to STM’s proven ecosystem (STM32Cube.AI, X-Cube-AI tools).
• Ideal for mid-range Edge AI tasks like image or voice classification.

nRF54 Series (Nordic Semiconductor)

• Combines ultra-low-power wireless connectivity with TinyML-enabled AI support.
• With Nordic’s recent acquisition of Neuton.AI, developers can deploy ultra-small (< 5 kB) models for sensor-based inference.
• Ideal for wearables, health monitors, and smart sensors.

RA8P1 (Renesas Electronics Corporation)

• Features a 1-GHz Arm Cortex-M85 coupled with a 250-MHz Arm Cortex-M33.
• Integrated Ethos-U55 NPU (256 GOPS at 500 MHz).
• Rich peripheral support including camera, display, voice and Gigabit Ethernet.

PSoC™ Edge E84 (Infineon Technologies)

• Combines Arm Cortex-M55 and M33 cores with an Ethos-U55 NPU and NNLite accelerator for low-power and high-performance AI inference.
• Supports advanced HMI, audio, and vision interfaces with built-in security and ModusToolbox™ AI development tools.

These families offer developers a mature toolchain, wide scalability options, and the ability to balance AI capabilities with control logic and connectivity, all of which are important for manufacturable, real-world products.

If your edge device needs continuous monitoring or always-on inference, specialized AI chips can offer unmatched efficiency.

If your design requires integration of multiple functions including sensing, connectivity, control, and AI, a general-purpose MCU with AI capabilities could be the ideal platform.

At NeuronicWorks, we take a holistic approach to developing products: we assess constraints, select the right technologies, and build solutions that deliver AI for the real world.