1,720,955 research outputs found
A low-cost, nanowatt, millimeter-scale memristive-vacuum sensor
No full textThis work demonstrates a low-power, light-weight, and cost-efficient vacuum pressure sensor and air leak detector based on 2 mm ×2 mm Cu/HfO2/ p^+ -Si memristor device. The operating principle of the proposed sensor relies on monitoring the off-state resistance ( R_off ) of the device, wherein the experimental results show that the R_off value is inversely proportional to the vacuum pressure. The fabricated sensors demonstrated a sensitivity of 493 Torr-1 and power consumption as low as ∼ 8 nW, when tested in a wide range of sub-atmospheric pressure ( 4.9× 10^-5 - 760 ) Torr, making them highly suitable for low-power applications. Furthermore, it is shown that the sensor can be integrated with a microcontroller-based circuitry to serve as a standalone sensing system.</p
SPIKA: an energy-efficient hybrid CMOS-RRAM compute-in-memory macro for machine learning
No full textThe deployment of neural networks (NNs) in machine learning (ML) applications such as computer vision, speech recognition and natural language processing has grown exponentially in the past few decades. The biggest challenge in implementing such algorithms is the constant data movement between the compute units and memory units. Today’s computing systems, primarily built based on the von Neumann architecture where data must be moved to a processing unit, have shown inefficiency in implementing ML algorithms. The speed and energy associated with this bottleneck present a key performance concern for a range of applications in artificial intelligence (AI) workloads. Another key challenge is that NNs carry out copious calculations of Multiply and Accumulate (MAC) operations which require high-performance GPUs, consuming a great amount of power. Therefore, there is an important need to improve computing efficiency in terms of both energy and latency. Innovation in new computing architectures is expected to play a major role in the future of ML hardware.
Non-volatile compute-in-memory (nvCIM) technology has recently shown promising results in addressing the data movement and multiply-and-accumulate (MAC) bottlenecks in machine learning algorithms by enabling parallel analogue vector-matrix multiplication (VMM) operations directly within memory arrays. By executing certain computational tasks within the memory itself, nvCIM provides an efficient alternative to traditional computing approaches. Specifically, nvCIM based on Resistive Random Access Memory (RRAM) has garnered attention due to its use of Ohm’s law for multiplication and Kirchhoff’s law for accumulation, allowing RRAM arrays to perform parallel in-memory MAC operations with significantly improved throughput and energy efficiency over digital computing methods. RRAM cells are used to carry weights of the neural network due to their low read voltage, ability to achieve multiple states per cell and dense structure.
In this research work, I present SPIKA, a novel energy-efficient RRAM-nvCIM chip designed for accelerating machine learning workloads. The main aim of this PhD project is to accelerate ML and ANN applications at the maximum possible power efficiency. The key innovation of SPIKA lies in its ability to efficiently transfer input signals to output signals with minimal overhead. The analogue computation is performed in the time domain, with the dot product accumulated on a switched capacitor, eliminating the need for high-resolution, power-intensive data converters. Ultimately, the key pillar of SPIKA is that it leverages the low-resolution niche it addresses to allow each domain to play to its strengths whilst using simple and efficient domain converters. This makes for a highly functional and simultaneously energy and area-efficient implementation.
The SPIKA chip has been fabricated using commercial 180nm technology and experimentally validated post-silicon. The core block features a 64x128 memory crossbar and utilizes 4-bit input, ternary weight, and 5-bit output resolutions. The results indicate a remarkable performance of SPIKA chip with a peak throughput of 1092 GOPS and energy efficiency of 195 TOPS/W. Compared to state-of-the-art solutions, the SPIKA core exhibits a significant energy efficiency improvement, ranging from 2.15x to 390x. For experimental demonstration, a neural network trained on the Modified National Institute of Standards and Technology (MNIST) database was implemented on the SPIKA chip. Results show a minimal 3\% loss in classification accuracy compared to the software baseline with 32-bit resolution, using the same network size and ternary quantized weights.
Furthermore, an 8-core SPIKA system, each core featuring a 64×128 array, is proposed to extend the architecture. The system-level architecture introduces minimal overhead on the SPIKA core by incorporating a streamlined switching mechanism within each core, enabling efficient analogue aggregation and inter-core communication without the need for additional circuitry. Circuit-level simulations demonstrate the SPIKA system's superior performance, achieving a peak normalized throughput of 8.736 TOPS and energy efficiency of 312 TOPS/W, demonstrating competitive performance even against designs on more advanced technology nodes
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
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