1,720,961 research outputs found
Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach
Full text linkApproximate Computing (AxC) techniques have become increasingly popular in trading off accuracy for performance gains in various applications. Selecting the best AxC techniques for a given application is challenging. Among proposed approaches for exploring the design space, Machine Learning approaches such as Reinforcement Learning (RL) show promising results. In this paper, we proposed an RL-based multi-objective Design Space Exploration strategy to find the approximate versions of the application that balance accuracy degradation and power and computation time reduction. Our experimental results show a good trade-off between accuracy degradation and decreased power and computation time for some benchmarks
Spiking Neural Network Data Reduction via Interval Arithmetic
Full text linkApproximate Computing (AxC) allows reducing the accuracy required by the user and the precision provided by the computing system to optimize the whole system in terms of performance, energy, and area reduction. Spiking Neural Networks(SNNs) are the new frontier for artificial intelligence because they better represent the timing influence on decision making, and also allow for a more reliable hardware design. Unfortunately, this design requires some area minimization strategies when the target hardware reaches the edge of computing. This seminal work introduces modeling of the approximation for data storage that supports an SNN via Interval Arithmetic (IA) by extracting the computation graph of the SNN and then resorting to IA to quickly evaluate the impact of approximation in terms of loss inaccuracy without executing the network each time. Experimental results comparing our model to the real network confirm the quality of the approach
Prediction of the Impact of Approximate Computing on Spiking Neural Networks via Interval Arithmetic
Full text linkApproximate Computing (AxC) techniques allow trade-off accuracy for performance, energy, and area reduction gains. One of the applications suitable for using AxC techniques are the Spiking Neural Networks (SNNs). SNNs are the new frontier for artificial intelligence since they allow for a more reliable hardware design. Unfortunately, this design requires some area minimization strategies when the target hardware reaches the edge of computing. In this work, we first extract the computation flow of an SNN, then employ Interval Arithmetic (IA) to model the propagation of the approximation error. This enables a quick evaluation of the impact of approximation. Experimental results confirm the model’s adherence and the capability of reducing the exploration time
Fast Exploration of the Impact of Precision Reduction on Spiking Neural Networks
Full text linkApproximate Computing (AxC) techniques trade off computational accuracy for gains in performance, energy efficiency, and area reduction. This trade-off is particularly advantageous when applications, like Spiking Neural Networks (SNNs), are naturally tolerant to some degree of accuracy loss. SNNs are especially practical when the target hardware is pushed to the edge of its computing capabilities, necessitating area minimization strategies. In this work, we utilize an Interval Arithmetic (IA)-based model that propagates approximation errors through the application’s computation flow to assess these approximations' impact on the outputs. We enhance this IA-based model by introducing observation points within the computation flow to quickly detect when the level of approximation surpasses a set threshold. Experimental results demonstrate the model’s effectiveness in significantly reducing exploration time, enabling more precise and fine-grained approximations that further minimize network parameters
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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