Conclusions, Contributions, and Future Work

Abstract

This dissertation has focused on techniques to minimize the energy consumption of deep learning algorithms for embedded applications on battery-constrained wearable edge devices. Although SotA in many typical machine-learning tasks, deep learning algorithms are also very costly in terms of energy consumption, due to their large amount of required computations and huge model sizes. Because of this, deep learning applications on battery-constrained wearables have only been possible through wireless connections with a resourceful cloud. This setup has several drawbacks. First, there are privacy concerns. This setup requires users to share their raw data—images, video, locations, and speech—with a remote system. As most users are not willing to share all of this, large-scale applications cannot yet be developed. Second, the cloud-setup requires users to be connected all the time, which is unfeasible given current cellular coverage. Furthermore, real-time applications require low latency connections, which cannot be guaranteed using the current communication infrastructure. Finally, this wireless connection is very inefficient—requiring too much energy per transferred bit for real-time data transfer on energy constrained platforms. All these issues—privacy, latency/connectivity, and costly wireless connections—can be resolved by moving towards computing on the edge.