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Research on activity recognition provides a wide range of ubiquitous computing applications. Once activities are recognized, computers can use this information to provide people with suitable services. In the past decade, many classification algorithms have been applied to activity recognition. However, most of them were based on the use of inertial measurement sensors, such as tri-axial accelerometers...
Although Deep Neural Networks (DNN) are ubiquitously utilized in many applications, it is generally difficult to deploy DNNs on resource-constrained devices, e.g., mobile platforms. Some existing attempts mainly focus on client-server computing paradigm or DNN model compression, which require either infrastructure supports or special training phases, respectively. In this work, we propose MoDNN —...
With the rapid growth of big-data applications, advanced data processing technologies, e.g., machine learning, are widely adopted in many industry fields. Although these technologies demonstrate powerful data analyzing and processing capability, there exist some security concerns that may potentially expose the user/owner of the services to information safety risk. In particular, the adoption of neuromorphic...
Neuromorphic architectures are widely used in many applications for advanced data processing, and often implements proprietary algorithms. In this work, we prevent an attacker with physical access from learning the proprietary algorithm implemented by the neuromorphic hardware. For this purpose, we leverage the obsolescence effect in memristors to judiciously reduce the accuracy of outputs for any...
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