Multiple Instance Learning For Efficient Sequential Data Classification On Resource-constrained Devices

Authors:
Don Dennis Microsoft Research
Chirag Pabbaraju Microsoft Research
Harsha Simhadri Microsoft Research India
Prateek Jain Microsoft Research

Introduction:

The authors study the problem of fast and efficient classification of sequential data (such astime-series) on tiny devices, which is critical for various IoT related applicationslike audio keyword detection or gesture detection.

Abstract:

We study the problem of fast and efficient classification of sequential data (such astime-series) on tiny devices, which is critical for various IoT related applicationslike audio keyword detection or gesture detection. Such tasks are cast as a standard classification task by sliding windows over the data stream to construct data points. Deploying such classification modules on tiny devices is challenging as predictions over sliding windows of data need to be invoked continuously at a high frequency. Each such predictor instance in itself is expensive as it evaluates large models over long windows of data. In this paper, we address this challenge by exploiting the following two observations about classification tasks arising in typical IoT related applications: (a) the "signature" of a particular class (e.g. an audio keyword) typically occupies a small fraction of the overall data, and (b) class signatures tend to be discernible early on in the data. We propose a method, EMI-RNN, that exploits these observations by using a multiple instance learning formulation along with an early prediction technique to learn a model that achieves better accuracy compared to baseline models, while simultaneously reducing computation by a large fraction. For instance, on a gesture detection benchmark [ 25 ], EMI-RNN improves standard LSTM model’s accuracy by up to 1% while requiring 72x less computation. This enables us to deploy such models for continuous real-time prediction on a small device such as Raspberry Pi0 and Arduino variants, a task that the baseline LSTM could not achieve. Finally, we also provide an analysis of our multiple instance learning algorithm in a simple setting and show that the proposed algorithm converges to the global optima at a linear rate, one of the first such result in this domain. The code for EMI-RNN is available at: https://github.com/Microsoft/EdgeML/tree/master/tf/examples/EMI-RNN

You may want to know: