This paper describes the use of Bernoulli-distributed binary random variables to implement signal processing from integrated arrays of parallel microsensors and VLSI imagers. parallel digital signal processing of thes...
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This paper describes the use of Bernoulli-distributed binary random variables to implement signal processing from integrated arrays of parallel microsensors and VLSI imagers. parallel digital signal processing of these signals is achieved in hardware by a combination of CMOS threshold detection of noisy raw signals, and the use of stochastic arithmetic (see Gaines, B.R., "advances in Information Systems Science", Plenum Press, vol.2, chap.2, p.37-172, 1969; Brown, B.D. and Card, H.C., IEEE Trans. Computers, vol.50, p.891-905, 2001). Hardware efficiencies include reduced power dissipation and improved error tolerance, as compared to binary radix implementations, together with the ability to trade precision against computation time using fixed hardware, reminiscent of biological computations.
In the evolution of artificial Intelligence (AI) and machine learning (ML), reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate obje...
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The Network of Polarized Evolutionary Processors (NPEP) is a rather new variant of the bio-inspired computing model called Network of Evolutionary Processors (NEP). This model, together with its variants, is able to p...
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ISBN:
(纸本)9781450372534
The Network of Polarized Evolutionary Processors (NPEP) is a rather new variant of the bio-inspired computing model called Network of Evolutionary Processors (NEP). This model, together with its variants, is able to provide theoretical feasible solutions to hard computational problems. NPEPE is a software engine able to simulate NPEP which is deployed over Giraph, an ultra-scalable platform based on the Bulk Synchronous parallel (BSP) programming model. Rather surprisingly, the BSP model and the underlying architecture of NPEP have many common points. Moreover, these similarities are also shared with all variants in the NEP family. We take advantage of these similarities and propose an extension of NPEPE (named gNEP) that enhances it to simulate any variant of the NEP's family. Our extended gNEP framework, presents a twofold contribution. Firstly, a flexible architecture able to extend software components in order to include other NEP models (including the seminal NEP model and new ones). Secondly, a component able to translate input configuration files representing the instance of a problem and an algorithm based on different variants of the NEP model into some suitable input files for gNEP framework. In this work, we simulate a solution to the "3-colorability" problem which is based on NPEP. We compare the results for a specific experiment using NPEPE engine and gNEP. Moreover, we show several experiments in the aim of studying, in a preliminary way, the scalability offered by gNEP to easily deploy and execute instances of problems requiring more intensive computations.
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 20...
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ISBN:
(数字)9783030937362
ISBN:
(纸本)9783030937355
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online.;The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops:;Workshop on advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021);Workshop on parallel, distributed and Federated Learning (PDFL 2021);Workshop on Graph Embedding and Mining (GEM 2021);Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021);Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021);Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021);Workshop on Bias and Fairness in AI (BIAS 2021);Workshop on Workshop on Active Inference (IWAI 2021);Workshop on Machine Learning for Cybersecurity (MLCS 2021);Workshop on Machine Learning in Software Engineering (MLiSE 2021);Workshop on MIning Data for financial applications (MIDAS 2021);Sixth Workshop on Data Science for Social Good (SoGood 2021);Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021);Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020);Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021)
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 20...
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ISBN:
(数字)9783030937331
ISBN:
(纸本)9783030937324
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online.;The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops:;Workshop on advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021);Workshop on parallel, distributed and Federated Learning (PDFL 2021);Workshop on Graph Embedding and Mining (GEM 2021);Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021);Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021);Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021);Workshop on Bias and Fairness in AI (BIAS 2021);Workshop on Workshop on Active Inference (IWAI 2021);Workshop on Machine Learning for Cybersecurity (MLCS 2021);Workshop on Machine Learning in Software Engineering (MLiSE 2021);Workshop on MIning Data for financial applications (MIDAS 2021);Sixth Workshop on Data Science for Social Good (SoGood 2021);Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021);Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020);Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021)
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