One of the biggest challenges embedded engineers face is utilizing powerful AI models that are often too big or power hungry for tiny embedded devices. AI at the edge means faster insights, lower latency, and improved privacy. And guess what? Embedded engineers are the key to making it happen.
So how do you optimize AI models for tiny devices? Thankfully, it’s gotten easier. In this guide, we’ll walk you through practical ways to do so, without sacrificing performance.
1. Understand the Constraints of Tiny Devices
First, let’s define what we mean by tiny. We’re talking about devices with 256KB RAM or less, limited processing power, and battery or energy-harvesting power sources. These aren’t even microprocessors, but can be MCUs like the STM32, ESP32, Nordic nRF series, or other ultra-low-power embedded platforms. The challenge? These devices weren't built for traditional AI workloads. Running even a simple neural network requires careful tuning to avoid exceeding hardware limitations or draining the battery.
2. Start with the Right Model Architecture
Model design matters. A Convolutional Neural Network (CNN) might work beautifully in the cloud, but it’s not going to fly on an MCU. Use edge-optimized architectures instead. There are various options; some models include:
FOMO (Faster Objects More Objects) — an anomaly detection algorithm designed for real-time object detection on devices with limited computational power, balancing speed and accuracy. It forgoes traditional bounding boxes in favor of centroid tracking, allowing for a smaller model that can still perform object detection.
MCUNet — developed for microcontrollers (MCUs), MCUNet combines a tailored neural architecture that enables deep learning on devices with extremely limited resources.
MobileNet — small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases.
If you want to compare model performance between embedded devices, you can check out representative benchmarking codes.
Tip: Always start simple. You can iterate and add complexity as needed, but keep your model lean from the beginning.
3. Reduce Model Size with Quantization
Quantization is one of the most effective tools for shrinking your model.
What is it? It’s the process of reducing the precision of your model’s weights and activations from 32-bit floats to 8-bit integers or even lower. The result?
- Smaller model file size
- Lower memory usage
- Faster inference
- Lower power draw
Most modern toolchains, including TensorFlow Lite and Edge Impulse, support quantization out of the box. Watch for:
- Slight dips in accuracy
- Hardware compatibility (some processors only support certain formats)
4. Use Pruning and Compression Techniques
Pruning involves removing weights or nodes from the model that contribute very little to the output. Think of it like decluttering your neural network. When done right, pruning can:
- Significantly reduce model size
- Improve inference speed
- Minimize overfitting
Other techniques like weight sharing, sparsity, and Huffman encoding can also cut down memory usage. Many of these optimizations can be done manually or through tools like the TensorFlow Model Optimization Toolkit or natively within Edge Impulse’s optimization pipeline.
5. Optimize for Your Target Hardware
Different hardware equals different performance profiles. Don’t assume your model will behave the same across all chips. For example:
- Memory, clock speed, and type of cores vary across hardware. Edge Impulse can help you select the right hardware for your project.
- NPUs (Neural Processing Units) are specialized accelerators that can handle inferencing more efficiently than general-purpose CPUs. Edge Impulse can help you build models that run on these accelerators where possible.
- Edge Impulse can also auto-generate deployment-ready models optimized for your exact hardware target — taking care of low-level tuning so you can focus on functionality.
6. Benchmark Performance Early and Often
Before diving into optimization techniques, capture baseline measurements early across three key dimensions: latency, memory usage, and power consumption. And remember, accuracy metrics must reflect your specific use case. Use profiling tools such as Eon Compiler to track RAM, ROM, and inference. Then iterate before deployment to ensure reliability. As an example of performance metrics, check out what it looks like on a typical model built on Edge Impulse.
7. Optimize Deployment for Your Hardware Target
Optimizing your AI model is only half the battle. Successful deployment to tiny devices requires careful planning around your specific hardware constraints.
Know your target environment — Are you deploying a microcontroller (MCU), application processor (CPU), or NPU-enabled chip? Each hardware platform has different capabilities around memory, clock speed, and power consumption.
Export in the right format — Once optimized, export your model in a format compatible with your target device, whether it’s C++ library, TensorFlow Lite, or Linux binaries for high-end edge processors.
Iterate for the edge, not the cloud — Unlike cloud environments, edge devices don’t allow for quick patches. Make sure the deployed model is:
- Fail-safe (can handle noisy or unexpected input)
- Responsive under varying operations conditions
- Lightweight enough to allow over-the-air (OTA) updates if needed
Pro tip: With Edge Impulse, you can deploy directly from Studio to a supported development board.
Small Devices Can Make a Big Impact
Optimizing AI models for tiny devices isn’t about cutting corners — it’s about engineering excellence. With the right techniques, tools, and mindset, you can build edge AI solutions that are smart, efficient, and production-ready.
At Edge Impulse, we make it easy to build, compress, and deploy models to resource-constrained hardware, so you can bring intelligence to even the tiniest device, without sacrificing performance.