(Image credit: Pixabay)
Embedded machine learning (ML) has evolved rapidly over the last decade, and rather than relying on cloud inference, developers are increasingly deploying AI models directly on low-power devices. This move is the result of new, innovative frameworks designed to run neural networks on microcontrollers and other embedded hardware. Despite their common goal, there are significant trade-offs between platforms such as Edge Impulse, TensorFlow Lite for Microcontrollers (TFLM), and other tools.
Edge Impulse is a full end-to-end TinyML development platform optimized for embedded devices. The platform provides a web-based workflow for data collection, model training, optimization, and deployment. Due to its EON compiler, Edge Impulse can also compress models to run on extremely small devices, reducing RAM and flash usage by a significant margin compared to standard TinyML frameworks.
Edge Impulse’s build-train-optimize-deploy loop enables users to deploy full ML workflows to edge devices. (Image credit: Edge Impulse)
With its automated pipeline, engineers can utilize Edge Impulse to quickly go from capturing sensor data to building and deploying a model, all without needing a degree in ML. Edge Impulse also supports multiple inference engines, like TFLM and CMSIS-NN, depending on the target device. On the other hand, TinyML frameworks such as TensorFlow Lite for Microcontrollers (TFLM) and CMSIS-NN are more low-level. TFLM is designed to run on microcontrollers with extremely limited memory (often under 1 MB of Flash and a few hundred kilobytes of RAM).
These frameworks are not tied to a cloud-based workflow as they offer developers increased control over how the model is optimized, quantized, and deployed. This makes them ideal for embedded systems where code size, latency, and power consumption are critical factors. There are also other emerging frameworks worth watching. uTensor is a lightweight C++ inference engine for MCUs, while Apache TVM lets developers tune tensor operations for embedded hardware.
Advanced research projects like MCUNet are pushing the envelope by leveraging neural architecture search and memory-efficient inference engines to run larger networks directly on microcontrollers.
Which one to pick? It depends on the use case. Edge Impulse takes the advantage when users need to prototype quickly, manage data visually, and deploy with minimal manual optimization. TFLM or CMSIS-NN takes the lead when the application demands increased control over memory, power, and performance on constrained devices. Of course, when experimenting or building for specific hardware, frameworks like microTVM or research toolkits like MCUNet edge out the others.
The embedded-ML landscape is still evolving. As TinyML grows, tools are converging toward easier development flows and better model efficiency. For embedded systems engineers, understanding the trade-offs of these frameworks is now as important as hardware design, more so when every byte and milliwatt counts.
