Hardware acceleration of Deep Neural Networks (DNNs) aims to tame their enormous
compute intensity. Fully realizing the potential of acceleration in this domain requires …

The high accuracy of deep neural networks (NNs) has led to the development of NN
accelerators that improve performance by two orders of magnitude. However, scaling these …

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 …

Deep Neural Networks (DNNs) have reinvigorated real-world applications that rely on
learning patterns of data and are permeating into different industries and markets. Cloud …

Deep Convolutional Neural Networks (CNNs) perform billions of operations for classifying a
single input. To reduce these computations, this paper offers a solution that leverages a …

In the past decade, monolithic three dimensional integrated circuits (M3D-ICs) advance fast
and demonstrate several important breakthroughs in the fabrication process and circuit level …

Speculative execution, the base on which modern high-performance general-purpose CPUs
are built on, has recently been shown to enable a slew of security attacks. All these attacks …

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

Important workloads, such as machine learning and graph analytics applications, heavily
involve sparse linear algebra operations. These operations use sparse matrix compression …

The increasing demand for extracting value out of ever-growing data poses an ongoing
challenge to system designers, a task only made trickier by the end of Dennard scaling. As …