Browse Theses

The increasing computing and storage capacity of electronic devices and information processing systems has increased their power consumption and energy usage dramatically. This has made the energy efficiency of circuit components and computing systems a very important concern. Energy efficiency is desirable for portable electronics (e.g., mobile phones, laptops, tablets, etc) because it lengthens the battery lifetime. In more sophisticated computing systems that are not battery operated (e.g., web servers, datacenters, etc), better energy efficiency reduces the total cost of ownership by reducing cost of electricity (due to computing and cooling), and improves the environmental impacts (e.g., reducing CO ₂ emission).

This dissertation is divided into two parts. In the first part, I discuss some of the recent challenges in designing low-power and energy-efficient circuits where I present some novel circuit-level techniques to reduce power consumption and improve energy efficiency. Two major techniques are discussed in this part. The first technique, charge recycling for power-gated circuits , reduces mode-transition (sleep to active and active to sleep) energy consumption in power gated circuits where we recycle electric charge—that will be wasted otherwise—between virtual ground and virtual supply at the edge of the mode transition. This, in theory, reduces the mode transition energy by 50%.

The next circuit-level technique presented in this dissertation introduces multimodal power gating structures using a novel design of a tri-modal power-gating switch . This switch is used to implement data-retentive power gating structures and multi-drowsy mode circuits. We also show that by using the proposed tri-modal switch, we can perform voltage scaling with the same infrastructure that is used for power gating.

The second part of this dissertation (system-level energy efficiency) is dedicated to energy efficiency in cloud computing infrastructures. We present a dynamically power-optimized datacenter that exploits correctly provisioned resources (servers and supplied cold air). The power optimization procedure comprises of two major actions. First is to predict the right number of required servers by employing a short-term workload forecasting technique. Second is to optimally choose candidate servers that are either being retired (turned OFF) or employed (turned ON) from the available pool of servers and to determine the optimum supplied cold-air temperature value of the Air Conditioning (AC) unit while satisfying the datacenter thermal constraints. The power saving is achieved by a combination of chassis consolidation and efficient cooling.