Compression Techniques Move From Research Into Production TinyML

TinyML — running neural network inference on microcontrollers with kilobytes of memory, milliwatts of power and no operating system — increasingly depends on three compression techniques applied together: quantization, pruning and knowledge distillation, paired with low-cost MCU platforms.

The three are not interchangeable, which is the part that gets lost when they are listed together. Quantization reduces numeric precision, typically from 32-bit float to 8-bit integer, cutting memory roughly fourfold and usually speeding inference because integer arithmetic suits these cores. Pruning removes weights or channels that contribute little. Distillation trains a small model to imitate a larger one, and often produces a better result than training the small architecture directly, because the large model's output distribution carries information the labels do not.

What makes this a production topic rather than a research one is that the constraint has stopped being accuracy and started being deployment. The techniques work; the difficulty is that each interacts with the others and with the target hardware in ways that are hard to predict, so the result has to be measured on the device rather than estimated. A model that meets its accuracy target on a workstation and does not fit the activation memory of the intended part is a common and expensive way to discover this late.