Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based
services. Energy consumption models are pivotal in designing and optimizing energy …

As the wireless sensor networks (WSNs) technology has great advancement, small and
smart WSN systems now can be used for more complicated and challenging applications …

Energy consumption has been widely studied in the computer architecture field for decades.
While the adoption of energy as a metric in machine learning is emerging, the majority of …

Given the performance and efficiency optimizations realized by the computer systems and
architecture community over the last decades, the dominating source of computing's carbon …

With Moore's law slowing down and Dennard scaling ended, energy-efficient domain-
specific accelerators, such as deep neural network (DNN) processors for machine learning …

General-purpose GPUs (GPGPUs) are becoming prevalent in mainstream computing, and
performance per watt has emerged as a more crucial evaluation metric than peak …

This paper introduces McPAT, an integrated power, area, and timing modeling framework
that supports comprehensive design space exploration for multicore and manycore …

In this paper, we explore the possibility of using STT-RAM technology to completely replace
DRAM in main memory. Our goal is to make STT-RAM performance comparable to DRAM …

Energy is increasingly a first-order concern in computer systems. Exploiting energy-accuracy
trade-offs is an attractive choice in applications that can tolerate inaccuracies. Recent work …

Hardware specialization, in the form of accelerators that provide custom datapath and
control for specific algorithms and applications, promises impressive performance and …