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 …

The gem5 simulation infrastructure is the merger of the best aspects of the M5 [4] and GEMS
[9] simulators. M5 provides a highly configurable simulation framework, multiple ISAs, and …

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

The end of Dennard scaling has made all systemsenergy-constrained. For data-intensive
applications with limitedtemporal locality, the major energy bottleneck is data …

With the rise of many-core chips that require substantial bandwidth from the network on chip
(NoC), integrated photonic links have been investigated as a promising alternative to …

The energy constraints due to the end of Dennard scaling, the popularity of in-memory
analytics, and the advances in 3D integration technology have led to renewed interest in …

As supercomputers grow, understanding their behavior and performance has become
increasingly challenging. New hurdles in scalability, programmability, power consumption …

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

As industry moves towards multicore chips, networks-on-chip (NoCs) are emerging as the
scalable fabric for interconnecting the cores. With power now the first-order design …