A cross-stack, network-centric architectural design for next-generation datacenters

This thesis proposes a full-stack, cross-layer datacenter architecture based on in-network computing and near-memory processing paradigms. The proposed datacenter architecture is built atop two principles: (1) utilizing commodity, off-the-shelf hardware (i.e., processor, DRAM, and network devices) with minimal changes to their architecture, and (2) providing a standard interface to the programmers for using the novel hardware. More specifically, the proposed datacenter architecture enables a smart network adapter to collectively compress/decompress data exchange between distributed DNN training nodes and assist the operating system in performing aggressive processor power management. It also deploys specialized memory modules in the servers, capable of performing general-purpose computation and network connectivity. This thesis unlocks the potentials of hardware and operating system co-design in architecting application-transparent, near-data processing hardware for improving datacenter's performance, energy efficiency, and scalability. We evaluate the proposed datacenter architecture using a combination of full-system simulation, FPGA prototyping, and real-system experiments

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others

Distributed smart camera network for safety and security

Current CCTV surveillance solutions are generally retrospective tools. Because real time use of CCTV requires human monitors to view a potentially exorbitant number of video feeds, CCTV is usually only useful after an incident has occurred. However, new technologies are making it possible for machines to perform some tasks that previously required a human monitor. The proposed project seeks to augment existing CCTV systems with behavioral analytics. The system uses a series of cameras, FPGAs, and computers to track object movement throughout a facility. This information is used to build a model of normal movement. Object movements are compared against this model and any ones that diverge from the model are flagged for review by security personnel