Energy and Performance Analysis of STTRAM Caches for Mobile Applications
August 08, 2019 Β· Declared Dead Β· π International Symposium on Embedded Multicore/Many-core Systems-on-Chip
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kyle Kuan, Tosiron Adegbija
arXiv ID
1908.04744
Category
cs.DC: Distributed Computing
Cross-listed
cs.ET
Citations
1
Venue
International Symposium on Embedded Multicore/Many-core Systems-on-Chip
Last Checked
4 months ago
Abstract
Spin-Transfer Torque RAMs (STTRAMs) have been shown to offer much promise for implementing emerging cache architectures. This paper studies the viability of STTRAM caches for mobile workloads from the perspective of energy and latency. Specifically, we explore the benefits of reduced retention STTRAM caches for mobile applications. We analyze the characteristics of mobile applications' cache blocks and how those characteristics dictate the appropriate retention time for mobile device caches. We show that due to their inherently interactive nature, mobile applications' execution characteristics---and hence, STTRAM cache design requirements---differ from other kinds of applications. We also explore various STTRAM cache designs in both single and multicore systems, and at different cache levels, that can efficiently satisfy mobile applications' execution requirements, in order to maximize energy savings without introducing substantial latency overhead.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Distributed Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Reproducing GW150914: the first observation of gravitational waves from a binary black hole merger
R.I.P.
π»
Ghosted
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
R.I.P.
π»
Ghosted
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
R.I.P.
π»
Ghosted
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
R.I.P.
π»
Ghosted
iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted