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

Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …

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 …

Sparse-sparse matrix multiplication (SpGEMM) is a computation kernel widely used in
numerous application domains such as data analytics, graph processing, and scientific …

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 …

Machine-Learning tasks are becoming pervasive in a broad range of domains, and in a
broad range of systems (from embedded systems to data centers). At the same time, a small …

This paper presents the Compute Cache architecture that enables in-place computation in
caches. Compute Caches uses emerging bit-line SRAM circuit technology to re-purpose …

Processing-in-memory (PIM) is rapidly rising as a viable solution for the memory wall crisis,
rebounding from its unsuccessful attempts in 1990s due to practicality concerns, which are …

With the emergence of data science, graph computing has become increasingly important
these days. Unfortunately, graph computing typically suffers from poor performance when …