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Deep learning has recently become immensely popular for image recognition, as well as for other recognition and pattern matching tasks in, e.g., speech processing, natural language processing, and so forth. The online evaluation of deep neural networks, however, comes with significant computational complexity, making it, until recently, feasible only on power-hungry server platforms in the cloud. In recent years, we see an emerging trend toward embedded processing of deep learning networks in edge devices: mobiles, wearables, and Internet of Things (IoT) nodes. This would enable us to analyze data locally in real time, which is not only favorable in terms of latency but also mitigates privacy issues. Yet evaluating the powerful but large deep neural networks with power budgets in the milliwatt or even microwatt range requires a significant improvement in processing energy efficiency.