Bøger af Glenn Reinman

In a post-apocalyptic world, humanity has proven resilient in the face of a global catastrophe. Isolated bastions of civilization have endured the centuries, despite a technological retrogression that has resulted in another Dark Ages. At the age of eighteen, strong-willed Cass was selected to be the General of the Army of the Sun. While the position of General is considered to be a glorified figurehead, Cass is determined to show her value as a leader. But the unaccepting patriarchy are willing to go to extreme measures to keep the status quo. Alon is a young engineer who has been studying the decaying and deadly ruins of the ancients. During his latest expedition, Alon makes a surprising discovery from the old world: a centuries-old neural replica of a scientist who lived before the apocalypse. But Alon is not the only interested party in this discovery, and other forces from the ancient world are closing in. Cass and Alon must find a way to work together, and discover whom they can trust in a dangerous new world of bio-engineered humans and neural reprogramming. A world where immortality is within reach.

Since the end of Dennard scaling in the early 2000s, improving the energy efficiency of computation has been the main concern of the research community and industry. The large energy efficiency gap between general-purpose processors and application-specific integrated circuits (ASICs) motivates the exploration of customizable architectures, where one can adapt the architecture to the workload. In this Synthesis lecture, we present an overview and introduction of the recent developments on energy-efficient customizable architectures, including customizable cores and accelerators, on-chip memory customization, and interconnect optimization. In addition to a discussion of the general techniques and classification of different approaches used in each area, we also highlight and illustrate some of the most successful design examples in each category and discuss their impact on performance and energy efficiency. We hope that this work captures the state-of-the-art research and development on customizable architectures and serves as a useful reference basis for further research, design, and implementation for large-scale deployment in future computing systems.