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Impact of Memory Approximation on Energy Efficiency

2018 Symposium on High Performance Computing Systems (WSCAD), 2018
Approximate memories can lower energy consumption at expense of incurring errors in some of the read/write operations. While these errors may be tolerated in some cases, in general, parts of the application must be re-executed to achieve usable results when a large number of errors occur.
Isaías B. Felzmann   +3 more
openaire   +1 more source

On the Lambert–Jonas approximation for ballistic impact

Mechanics Research Communications, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ben-Dor, G., Dubinsky, A., Elperin, T.
openaire   +2 more sources

The impact of excluding common blocks for approximate matching

Computers & Security, 2020
Abstract Approximate matching functions allow the identification of similarity (bytewise level) in a very efficient way, by creating and comparing compact representations of objects (a.k.a digests). However, many similarity matches occur due to common data that repeats over many different files and consist of inner structure, header and footer ...
Vitor Hugo Galhardo Moia   +2 more
openaire   +1 more source

Approximation et existence en vibro-impact

Comptes Rendus de l'Académie des Sciences - Series I - Mathematics, 1999
Summary: We consider a dynamical system with a finite number of degrees of freedom, in generalized coordinates, subject to unilateral constraints, which are smooth, but not necessarily convex. When the constraints are saturated, we define an impact law involving a restitution coefficient \(e\in[0,1]\).
Paoli, Laetitia, Schatzman, Michelle
openaire   +2 more sources

An Approximate Treatment of Blunt Body Impact

Journal of Elasticity, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Knops, R. J., Villaggio, Piero
openaire   +2 more sources

Analyzing the Impact of Approximate Adders on the Reliability of FPGA Accelerators

2021 IEEE European Test Symposium (ETS), 2021
In this paper, we evaluate the impact of approximate adders on the reliability of FPGA-based accelerators for applications that present inherent error resilience. We perform an exhaustive fault injection campaign to examine the effects of single bit upsets (SEUs) in the adders in the DCT block of a JPEG encoder IP core.
Ioannis Tsounis   +2 more
openaire   +1 more source

On the Impact of Approximate Computation in an Analog DeSTIN Architecture

IEEE Transactions on Neural Networks and Learning Systems, 2014
Deep machine learning (DML) holds the potential to revolutionize machine learning by automating rich feature extraction, which has become the primary bottleneck of human engineering in pattern recognition systems. However, the heavy computational burden renders DML systems implemented on conventional digital processors impractical for large-scale ...
Steven R. Young   +3 more
openaire   +2 more sources

Classical Approximation for Ionization by Proton Impact

Physical Review, 1968
Ionization of atoms by proton impact, as predicted by the classical binary-encounter approximation, is examined and compared with available experimental data on the noble-gas and alkali-metal atoms. The results indicate that these predictions agree with observation to within a factor of 2 or 3, and are as reliable as the comparable results for electron
J. D. Garcia, E. Gerjuoy, J. E. Welker
openaire   +1 more source

Impact of Approximation Error on the Decisions of LDPC Decoding

Journal of Signal Processing Systems, 2010
In this paper the impact of the approximation error on the decisions taken by LDPC decoders is studied. In particular, we analyze the mechanism, by means of which approximation error alters the decisions of a finite-word-length implementation of the decoding algorithm, with respect to the decisions taken by the infinite precision case, approximated ...
Nikos Kanistras, Vassilis Paliouras
openaire   +1 more source

Impact of Layers Selective Approximation on CNNs Reliability and Performance

2020 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), 2020
In this paper, we evaluate the impact on reliability and performance of the selective approximation of Convolutional Neural Networks (CNNs) layers on NVIDIA mixed-precision architectures. We found that, even without affecting accuracy, the approximation from single to half precision of each layer has a different impact on both performance and output ...
Paolo Rech
exaly   +2 more sources

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