1,720,958 research outputs found
Z-PIM: A Sparsity-Aware Processing-in-Memory Architecture With Fully Variable Weight Bit-Precision for Energy-Efficient Deep Neural Networks
No full textWe present an energy-efficient processing-in-memory (PIM) architecture named Z-PIM that supports both sparsity handling and fully variable bit-precision in weight data for energy-efficient deep neural networks. Z-PIM adopts the bit-serial arithmetic that performs a multiplication bit-by-bit through multiple cycles to reduce the complexity of the operation in a single cycle and to provide flexibility in bit-precision. To this end, it employs a zero-skipping convolution SRAM, which performs in-memory AND operations based on custom 8T-SRAM cells and channel-wise accumulations, and a diagonal accumulation SRAM that performs bit- and spatial-wise accumulation on the channel-wise accumulation results using diagonal logic and adders to produce the final convolution outputs. We propose the hierarchical bitline structure for energy-efficient weight bit pre-charging and computational readout by reducing the parasitic capacitances of the bitlines. Its charge reuse scheme reduces the switching rate by 95.42% for the convolution layers of VGG-16 model. In addition, Z-PIM's channel-wise data mapping enables sparsity handling by skip-reading the input channels with zero weight. Its read-operation pipelining enabled by a readsequence scheduling improves the throughput by 66.1%. The Z-PIM chip is fabricated in a 65-nm CMOS process on a 7.568-mm(2) die, while it consumes average 5.294-mW power at 1.0-V voltage and 200-MHz frequency. It achieves 0.31-49.12-TOPS/W energy efficiency for convolution operations as the weight sparsity and bit-precision vary from 0.1 to 0.9 and 1 to 16 bit, respectively. For the figure of merit considering input bit- width, weight bit-width, and energy efficiency, the Z-PIM shows more than 2.1 times improvement over the state-of-the-art PIM implementations.
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
인공지능 학습과 추론을 위한 다중 데이터 플로우 통합 인-메모리 연산 아키텍처
No full text학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2022.2,[iv, 28 p. :]As artificial intelligence and machine learning technology are revolutionizing our daily life, many accelerator architectures have been proposed for faster and more energy-efficient processing for the workloads. However, the von Neumann bottleneck often limits their system performance, which states the unavoidable data bandwidth problem caused by separated computation and memory units. The processing-inmemory paradigm, which merges logic into memory, becomes increasingly popular to address this system bottleneck. In this paper, we propose a unified processing-in-memory (U-PIM) architecture, which supports both inference and training for various deep learning models, including MLPs, CNNs, RNNs, and transformers. U-PIM comprises an array of SRAM-based PIM macros and an embedded DRAM, where the macros work on the tiled workloads and the eDRAM provides a global memory space. U-PIM allows various data flows based on the proposed tile scheduling algorithms, including forward propagation, error propagation, and weight update for end-to-end on-chip training. It also supports variable bit precision ranging from 1-bit to 16-bit for inference scenarios with quantized models. Throughout the entire processing, UPIM efficiently handles sparsity for better performance and energy efficiency. To validate the U-PIM architecture, we implement the U-PIM macro that contains an 8T-cell-based 3-way processing memory and a 6T-cell-based weight update memory along with bit-serial-based accumulation logic in a compact footprint of 0.315mm in 28nm process. With the 64 macros in an 8×8 array, U-PIM achieves 0.31-18.18 TOPS inference performance for several layers from popular models. Finally, we demonstrate that U-PIM can successfully train the VGG16 model for the CIFAR100 dataset with a negligible loss in accuracy. As a result, it achieves 1.29 TOPS/W power efficiency and 7.65 GOPS/mm area efficiency in the training, which are 186.24 times more power efficient and 2.8 times more area efficient than Nvidia TITAN X GPU.한국과학기술원 :전기및전자공학부
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