Fast data aware neural architecture search via supernet accelerated evaluation

Tiny machine learning (TinyML) promises to revolutionize fields such as healthcare, environmental monitoring, and industrial maintenance by running machine learning models on low-power embedded systems. However, the complex optimizations required for successful TinyML deployment continue to impede its widespread adoption.

A promising route to simplifying TinyML is through automatic machine learning (AutoML), which can distill elaborate optimization workflows into accessible key decisions. Notably, Hardware Aware Neural Architecture Searches — where a computer searches for an optimal TinyML model based on predictive performance and hardware metrics — have gained significant traction, producing some of today's most widely used TinyML models.

TinyML systems operate under extremely tight resource constraints, such as a few kB of memory and an energy consumption in the mW range. In this tight design space, the choice of input data configuration offers an attractive accuracy-latency tradeoff. Achieving truly optimal TinyML systems thus requires jointly tuning both input data and model architecture.

Despite its importance, this “Data Aware Neural Architecture Search” remains underexplored. To address this gap, we propose a new state-of-the-art Data Aware Neural Architecture Search technique and demonstrate its effectiveness on the novel TinyML “Wake Vision” dataset. Our experiments show that across varying time and hardware constraints, Data Aware Neural Architecture Search consistently discovers superior TinyML systems compared to purely architecture-focused methods, underscoring the critical role of data-aware optimization in advancing TinyML.