Open source software frameworks are the key for applying deep learning technologies. Orion Wolfe and Shohei Hido introduce Chainer, a Python-based standalone framework that enables users to intuitively implement many kinds of other models, including recurrent neural networks, with a lot of flexibility and comparable performance to GPUs.

Neural networks are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. Song Han explains how deep compression addresses this limitation by reducing the storage requirement of neural networks without affecting their accuracy and proposes an energy-efficient inference engine (EIE) that works with this model.

Recent high-profile data breaches have made it clear that traditional security based on n-tier application partitioning is broken. As we move into the container era, there is a huge opportunity to revolutionize security by rendering developer intent directly into the network fabric. Andrew Randall presents an open source approach to this problem, leveraging proven IP networking and Linux concepts.