Deep learning is now present in a wide range of services and applications, replacing and complementing other machine learning algorithms. Performing training and inference of deep neural networks using the cloud computing model is not viable for applications where low latency is required. Furthermore, the rapid proliferation of the Internet of Things will generate a large volume of data to be processed, which will soon overload the capacity of cloud servers. One solution is to process the data at the edge devices themselves, in order to alleviate cloud server workloads and improve latency. However, edge devices are less powerful than cloud servers, and many are subject to energy constraints. Hence, new resource and energy-oriented deep learning models are required, as well as new computing platforms. This paper reviews the main research directions for edge computing deep learning algorithms.

For a significant number of electronic systems used in safety-critical applications circuit testing is performed periodically. For these systems, power dissipation due to Built-In Self Test (BIST) can represent a significant percentage of the overall power dissipation. One approach to minimize power consumption in these systems consists of test pattern sequence reordering. Moreover, a key observation is that test patterns are in general expected to exhibit don't cares, which can naturally be exploited during test pattern sequence reordering. In this paper we develop an optimization model and describe an efficient algorithm for reordering pattern sequences in the presence of don't cares. Preliminary experimental results amply confirm that the resulting power savings due to pattern sequence reordering using don't cares can be significant.

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