The explosion of IoT sensors in industrial, consumer and remote sensing use cases has come with unprecedented demand for computing infrastructure to transmit and to analyze petabytes of data. Concurrently, the world is slowly shifting its focus towards more sustainable computing. For these reasons, there has been a recent effort to reduce the footprint of related computing infrastructure, especially by deep learning algorithms, for advanced insight generation. The `TinyML' community is actively proposing methods to save communication bandwidth and excessive cloud storage costs while reducing algorithm inference latency and promoting data privacy. Such proposed approaches should ideally process multiple types of data, including time series, audio, satellite images, and video, near the network edge as multiple data streams has been shown to improve the discriminative ability of learning algorithms, especially for generating fine grained results. Incidentally, there has been recent work on data driven conditional computation of subnetworks that has shown real progress in using a single model to share parameters among very different types of inputs such as images and text, reducing the computation requirement of multi-tower multimodal networks. Inspired by such line of work, we explore similar per patch conditional computation for the first time for mobile vision transformers (vision only case), that will eventually be used for single-tower multimodal edge models. We evaluate the model on Cornell Sap Sucker Woods 60, a fine grained bird species discrimination dataset. Our initial experiments uses $4X$ fewer parameters compared to MobileViTV2-1.0 with a $1$% accuracy drop on the iNaturalist '21 birds test data provided as part of the SSW60 dataset.

本研究针对工业、消费和遥感领域中物联网传感器导致的计算基础设施需求激增问题，提出了一种新颖的移动视觉变换器模型的条件计算方法。该方法在减少参数数量的同时，保持了较高的准确性，支持多种数据类型的处理，从而为可持续计算和高效数据监测提供了潜在的解决方案。

面向边缘计算的野生动物监测视觉专家混合方法