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Approximate Computing

Oliver Keszocze
2022 it - Information Technology  
This greatly hinders the idea of Edge Computing, where computations are carried out on cheap, small and often battery powered devices "on the edge", i. e. in the field.  ...  Fortunately, many practical applications can tolerate a certain degree of incorrectness in computations.  ...  These opportunities for incorrectness are exploited by the Approximate Computing design paradigm.  ... 
doi:10.1515/itit-2022-0027 fatcat:2afjt4su45e5bophbdsrotgx6q

Approximate Computing, Intelligent Computing

Lieven Eeckhout
2018 IEEE Micro  
Ravi Nair further argues that approximate computing could be considered intelligent computing, because it uses energy resources to perform exact computation only when needed and approximate whenever possible  ...  Over the past couple years, approximate computing emerged as a novel computing systems paradigm that trades off quality for efficiency.  ... 
doi:10.1109/mm.2018.043191119 fatcat:mlpbh5jnybc3felb7dg6uly7fy

Approximate Bayesian Computation

Mikael Sunnåker, Alberto Giovanni Busetto, Elina Numminen, Jukka Corander, Matthieu Foll, Christophe Dessimoz, Shoshana Wodak
2013 PLoS Computational Biology  
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics.  ...  This is a "Topic Page" article for PLOS Computational Biology. History The first Approximate Bayesian computation (ABC)-related ideas date back to the 1980s.  ...  This article started as assignment for the graduate course ''Reviews in Computational Biology'' (263-5151-00L) at ETH Zurich.  ... 
doi:10.1371/journal.pcbi.1002803 pmid:23341757 pmcid:PMC3547661 fatcat:y4ooxahn2nh6dlib7d45uzycfm

Hardware approximate computing

Hassaan Saadat, Sri Parameswaran
2017 Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion - CASES '17  
To this end, this paper discusses an open source toolkit, called iACT (Intel's Approximate Computing Toolkit) to analyze and study the scope of approximations in applications.  ...  As an example on how to use this toolkit, we include two different applications and analyze the scope of approximate computing in these.  ...  Why study approximate computing now?  ... 
doi:10.1145/3125501.3125518 dblp:conf/cases/SaadatP17 fatcat:iqovqqqtbze2npgh4ysmrx3p4m

Approximating the Likelihood in Approximate Bayesian Computation [article]

Christopher C Drovandi, Clara Grazian, Kerrie Mengersen, Christian Robert
2018 arXiv   pre-print
This chapter will appear in the forthcoming Handbook of Approximate Bayesian Computation (2018).  ...  The conceptual and methodological framework that underpins approximate Bayesian computation (ABC) is targetted primarily towards problems in which the likelihood is either challenging or missing.  ...  Introduction Approximate Bayesian computation (ABC) is now a mature algorithm for likelihood-free estimation.  ... 
arXiv:1803.06645v1 fatcat:yyzawutmkfefbfq3rdvsueawqe

Defensive Approximation: Securing CNNs using Approximate Computing [article]

Amira Guesmi, Ihsen Alouani, Khaled Khasawneh, Mouna Baklouti, Tarek Frikha, Mohamed Abid, Nael Abu-Ghazaleh
2021 arXiv   pre-print
We show that our approximate computing implementation achieves robustness across a wide range of attack scenarios.  ...  In this paper, we propose for the first time to use hardware-supported approximate computing to improve the robustness of machine learning classifiers.  ...  Approximate Computing The speed of new generations of computing systems, from embedded and mobile devices to servers and computing data centers, has been drastically climbing in the past decades.  ... 
arXiv:2006.07700v2 fatcat:k4x7ntzlrrb6vdkluhx46wlsze

Approximate Bayesian computation methods

Gilles Celeux
2012 Statistics and computing  
This issue is proposing a Special Issue on Approximate Bayesian Computation (ABC) methods, which has not been conceived according to this scheme.  ...  The project of this special issue simply rises because, since early 2010, Statistics and Computing has received a lot of submissions on this new topic of computational statistics.  ...  McCoy propose an ABC approximation to perform biased filtering for a hidden Markov model when the likelihood function is intractable.  ... 
doi:10.1007/s11222-012-9350-8 fatcat:wtasjl42zvb63pgff54dkibq6i

Approximation Bayesian computation
English

P Marjoram
2013 OA Genetics  
Approximation Bayesian computation [ABC] is an analysis approach that has arisen in response to the recent trend to collect data that is of a magnitude far higher than has been historically the case.  ...  This conflict has led to the rise of an alternative approach called approximate Bayesian computation (ABC).  ...  This review discusses the approximate Bayesian computational approach. Discussion The author has referenced some of his own studies in this review.  ... 
doi:10.13172/2054-197x-1-1-853 pmid:25606346 pmcid:PMC4297650 fatcat:w7rgrdamnrhuharbtyedofpb2i

Computing Approximate Statistical Discrepancy [article]

Michael Matheny, Jeff M. Phillips
2018 arXiv   pre-print
First compute random samples N and S of size O( 1 ε log 1 ε ) and O( 1 ε 2 ) respectively.  ...  That is, in each of O(1/ε d ) grid cells, we compute the sum of counts for all grid cells with smaller or equal indexes in each dimension.  ...  At depth i in the recurrence we compute the grid (Lemma 15 with parameter i), and find the grid spanning rectangle (Lemma 17 with = r/2 i ), which implies that The dominant term is then the preprocessing  ... 
arXiv:1804.11287v3 fatcat:eiebvpzyerhejiiqq5fp77z7xa

Adaptive approximate Bayesian computation

M. A. Beaumont, J.-M. Cornuet, J.-M. Marin, C. P. Robert
2009 Biometrika  
While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior.  ...  Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version.  ...  cost of this step of the population Monte Carlo approximate Bayesian computation algorithm is in O(T N 2 ).  ... 
doi:10.1093/biomet/asp052 fatcat:atzdxxkhdbhn5e2nh673qfqufy

Computing Approximate Statistical Discrepancy

Michael Matheny, Jeff M. Phillips, Michael Wagner
2018 International Symposium on Algorithms and Computation  
We develop general algorithms for this approximation problem for range spaces with bounded VC-dimension, as well as significant improvements for specific geometric range spaces defined by balls, halfspaces  ...  I S A A C 2 0 1 8 32:8 Computing Approximate Statistical Discrepancy Interval Preprocessing and Merging.  ...  When Φ is 32:2 Computing Approximate Statistical Discrepancy replaced with a statistically motivated form [12, 13] , then this task (typically focusing on disks or rectangles) is the core subroutine  ... 
doi:10.4230/lipics.isaac.2018.32 dblp:conf/isaac/MathenyP18 fatcat:unikkzbwn5hczleph3ftzp5ave

Computing approximate PSD factorizations [article]

Amitabh Basu, Michael Dinitz, Xin Li
2016 arXiv   pre-print
We give an algorithm for computing approximate PSD factorizations of nonnegative matrices.  ...  The running time of the algorithm is polynomial in the dimensions of the input matrix, but exponential in the PSD rank and the approximation error.  ...  Our main result is a polynomial time algorithm to compute approximate factorizations of matrices with fixed PSD rank.  ... 
arXiv:1602.07351v1 fatcat:tk7eiurlbjg3lbraxsqrioqym4

Computing Approximate PSD Factorizations

Amitabh Basu, Michael Dinitz, Xin Li, Marc Herbstritt
2016 International Workshop on Approximation Algorithms for Combinatorial Optimization  
We give an algorithm for computing approximate PSD factorizations of nonnegative matrices.  ...  The running time of the algorithm is polynomial in the dimensions of the input matrix, but exponential in the PSD rank and the approximation error.  ...  Our main result is a polynomial time algorithm to compute approximate factorizations of matrices with fixed PSD rank.  ... 
doi:10.4230/lipics.approx-random.2016.2 dblp:conf/approx/BasuD016 fatcat:h3qwc2ztsbfqbda4s5tiks6hue

Approximate Bayesian Computational methods [article]

Jean-Michel Marin, Christian P. Robert
2011 arXiv   pre-print
Also known as likelihood-free methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to untractable likelihood problems, first in  ...  It was first proposed in population genetics by Tavaré et al (1997) , who introduced approximate Bayesian computational methods as a rejection technique bypassing the computation of the likelihood function  ...  Tolerance threshold and ABC approximation error As noted above, the choice of the tolerance level ε is mostly a matter of computational power: smaller ε's are associated with higher computational costs  ... 
arXiv:1101.0955v2 fatcat:5yp23zu7pferxfep7y7uf6aj64

Towards Approximate Mobile Computing [article]

Veljko Pejovic
2019 arXiv   pre-print
This novel way of thinking about computation is termed Approximate Computing (AC) and promises to reduce resource usage, while ensuring that satisfactory performance is delivered to end-users.  ...  Mobile computing is one of the main drivers of innovation, yet the future growth of mobile computing capabilities remains critically threatened by hardware constraints, such as the already extremely dense  ...  STARTING POINT -CONVENTIONAL APPROXIMATE COMPUTING A range of approximate computing techniques (ACTs) operating at all levels of the computing stack have been developed in the last ten years [5] ,.  ... 
arXiv:1901.08972v1 fatcat:qpaez6ncazf6nf5qbxn3tllriy
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