  There is a growing interest in low power highly efficient wearable devices
for automatic dietary monitoring (ADM) [1]. The success of deep neural networks
in audio event classification problems makes them ideal for this task. Deep
neural networks are, however, not only computationally intensive and energy
inefficient but also require a large amount of memory. To address these
challenges, we propose a shallow gated recurrent unit (GRU) architecture
suitable for resource-constrained applications. This paper describes the
implementation of the Tiny Eats GRU, a shallow GRU neural network, on a low
power micro-controller, Arm Cortex M0+, to classify eating episodes. Tiny Eats
GRU is a hybrid of the traditional GRU [2] and eGRU [3] to make it small and
fast enough to fit on the Arm Cortex M0+ with comparable accuracy to the
traditional GRU. The Tiny Eats GRU utilizes only 4% of the Arm Cortex M0+
memory and identifies eating or non-eating episodes with 6 ms latency and
accuracy of 95.15%.
