Bring intelligence to the smallest devices. Deploy optimized AI models on microcontrollers, sensors, and embedded systems with limited memory, power, and processing capabilities.
Embedded devices operate under severe resource constraints that make traditional AI deployment impossible. Yet these tiny computers are everywhere—and desperately need intelligence.
Microcontrollers typically have 256KB-2MB of flash storage and 32KB-512KB of RAM. A single unoptimized AI model can be hundreds of megabytes—thousands of times larger than available memory.
Embedded processors run at 10-200 MHz (vs 3000+ MHz for desktop CPUs) with no GPU acceleration. Matrix operations that take milliseconds on servers can take seconds on microcontrollers.
Battery-powered sensors and wearables have strict energy budgets (often under 100mW). Running unoptimized AI models drains batteries in hours instead of months or years.
Embedded devices are often manufactured in millions of units where every cent matters. Adding expensive AI-capable hardware isn't economically viable for most applications.
Models must be compressed by 100-1000x to fit!
Through aggressive optimization, specialized algorithms, and purpose-built frameworks, we deploy production-grade AI models on devices previously considered too constrained for machine learning.
We employ multi-stage optimization pipelines to achieve 100-1000x model size reduction:
118x smaller, fits on microcontroller!
We optimize models specifically for target embedded hardware architectures:
CMSIS-NN optimizations for ARM microcontrollers (M0, M4, M7), leveraging SIMD instructions and hardware DSP
Custom operators for RISC-V processors, optimized for open-source embedded ecosystems
Ultra-lightweight models for 8-bit microcontrollers with under 32KB RAM
WiFi-enabled IoT processors with TensorFlow Lite Micro optimizations
We leverage specialized frameworks designed for embedded AI deployment:
Battery-powered embedded devices require aggressive power management:
Smartwatches and fitness trackers run AI models for heart rate anomaly detection, fall detection, sleep stage classification, and activity recognition—all on battery-powered microcontrollers.
Environmental sensors detect air quality issues, vibration sensors predict equipment failures, and acoustic sensors identify specific sounds (glass breaking, gunshots) locally without cloud connectivity.
Solar-powered field sensors use TinyML to detect crop diseases from leaf images, monitor soil conditions, and identify pest infestations—operating for months on a single charge in remote locations.
Tiny vibration sensors attached to motors and pumps run anomaly detection models locally, predicting failures days in advance and transmitting only alerts rather than continuous data streams.
Always-listening wake word detection ("Hey Assistant") runs on microcontrollers using under 1mW of power, waking the main processor only when the trigger phrase is detected.
Battery-powered camera traps use embedded AI to identify animal species locally, storing only images of target species and dramatically extending battery life from weeks to months.
We profile your target embedded platform: CPU architecture, clock speed, RAM/Flash availability, power budget, and peripheral capabilities. This establishes hard constraints for model optimization.
We select or design ultra-efficient architectures optimized for your use case and constraints. This might involve adapting existing TinyML models or creating custom architectures from scratch.
Models are trained with quantization simulation, allowing them to learn robust representations that maintain accuracy even with 8-bit or 4-bit precision. This prevents the accuracy collapse that post-training quantization can cause.
We apply pruning (removing redundant weights), quantization (reducing precision), and knowledge distillation (training smaller models) in multi-stage pipelines, achieving 100-1000x compression while maintaining 90-95% original accuracy.
We profile the model on actual target hardware, measuring memory usage, inference latency, and power consumption. Bottlenecks are identified and optimized through operator fusion, memory layout optimization, and custom kernels.
Models are compiled to efficient C/C++ code and deployed to embedded devices. We implement over-the-air update mechanisms for model improvements and bug fixes, with rollback capabilities for safety.
With aggressive optimization, we've deployed models on devices with as little as 16KB of RAM and 256KB of flash storage. However, for practical applications with good accuracy, we recommend at least 64KB RAM and 512KB flash. ARM Cortex-M4/M7 processors with hardware floating-point units provide the best performance for the cost.
With proper quantization-aware training and iterative pruning, we typically maintain 90-97% of the original model's accuracy even with 100x+ compression. The key is designing for constraints from the start rather than trying to compress an oversized model after training. Some applications tolerate more accuracy loss than others—we optimize based on your requirements.
Yes, we implement secure OTA (over-the-air) update mechanisms that download compressed model updates when devices connect to WiFi or cellular networks. Updates can be differential (only changed parameters) to minimize bandwidth. We include versioning, rollback capabilities, and A/B testing to ensure safety and reliability.
Classification and anomaly detection models work extremely well. Simple regression models are also highly effective. Computer vision is possible with optimized architectures (MobileNet-Tiny, etc.). NLP is challenging but feasible for specific tasks like wake word detection or simple text classification. Generative models (GANs, LLMs) are generally too large for current embedded hardware.
Timeline depends on model complexity and optimization requirements. A typical project takes 8-16 weeks: 2-3 weeks for requirements and hardware profiling, 4-8 weeks for model development and optimization, 2-3 weeks for on-device testing and refinement, 1-2 weeks for deployment tooling. Simple models can be deployed faster; complex custom architectures may take longer.
Our embedded AI specialists will assess your hardware constraints, optimize models for your platform, and deploy production-ready TinyML solutions that fit within your memory, power, and cost budgets.
Explore related Edge AI topics: