How to Deploy Trained Neural Networks on Arduino and Raspberry Pi

Model Too Big, Chip Too Small

Server‑side PyTorch models are disastrous on embedded devices. An Arduino Uno with 2 KB SRAM cannot even hold the parameters of a single convolutional layer, and even a Raspberry Pi with 1 GB RAM struggles to run ResNet.

The compute and storage capabilities of embedded chips are orders of magnitude lower than GPU servers, making direct deployment infeasible.

Quantization + Pruning + Distillation: Three Techniques for Model Slimming

The first step is to “lose weight.” Quantization converts 32‑bit floats to 8‑bit integers, cutting model size by about 75 % and boosting inference speed severalfold.

However, naïve quantization can drop accuracy dramatically (e.g., from 92 % to 65 %). Quantization‑aware training (QAT) simulates quantization during training and typically limits accuracy loss to 1–2 percentage points.

Beyond quantization, structural modifications are needed. Pruning removes near‑zero weights, usually eliminating 30–50 % of connections with negligible impact on accuracy.

Knowledge distillation trains a small “student” model using a large “teacher” model; a few‑hundred‑KB student can inherit about 90 % of the teacher’s capability. Lightweight architectures such as MobileNet and SqueezeNet are designed for embedded use.

In a personal image‑classification project, a 150 MB model was reduced to 800 KB after quantization, pruning, and distillation, enabling real‑time inference on a Raspberry Pi.

Deployment Characteristics and Solutions for Different Devices

TensorFlow Lite is the mainstream runtime for embedded AI, supporting platforms from ARM Cortex‑M microcontrollers to Raspberry Pi. For MCUs like Arduino, TensorFlow Lite Micro can run simple CNNs within a few kilobytes of memory, suitable for voice‑wake or gesture‑recognition tasks.

Raspberry Pi runs a full Linux OS, allowing Python‑based inference. Because its ARM CPU is limited, adding a Coral USB accelerator or Intel Neural Compute Stick can increase inference speed by more than ten times, achieving 30 fps real‑time object detection.

Arduino’s 2 KB memory restricts it to a few fully‑connected layers or a single CNN, making it appropriate for lightweight sensor‑data classification. The Arduino Nano 33 BLE Sense, with larger memory and built‑in sensors, can run slightly more complex pre‑trained tiny models.

If budget permits, development boards with hardware accelerators—such as Google Coral Dev Board or NVIDIA Jetson Nano—offer mature ecosystems for projects like smart locks or miniature autonomous vehicles.

Hidden Challenges and Optimizations in Deployment

Deployment is more complex than training. Power consumption is critical; battery‑powered Arduinos can deplete quickly if the model runs frequently.

Real‑time constraints matter: inference latency over 100 ms degrades user experience.

Debugging on embedded devices is difficult without convenient tools; the author experienced repeated Arduino reboots caused by oversized intermediate layer outputs, which took two days to locate.

Choosing the right toolchain matters. Besides TensorFlow Lite, ONNX Runtime supports a broader range of model formats, Edge Impulse is beginner‑friendly, while PyTorch Mobile’s embedded support lags behind TensorFlow.

Further optimizations such as operator fusion and memory reuse can squeeze additional performance. Sometimes a trade‑off between accuracy and speed is needed—for example, raising the confidence threshold in object detection reduces false positives but can increase speed by about 30 %.

In essence, fitting AI models onto embedded boards is like dancing with shackles: every bit of compute and storage must be maximally exploited.