1,720,967 research outputs found
Quantization:how far should we go?
No full textMachine learning, and specifically Deep Neural Networks (DNNs) impact all parts of daily life. Although DNNs can be large and compute intensive, requiring processing on big servers (like in the cloud), we see a move of DNNs into loT-edge based systems, adding intelligence to these systems. These systems are often energy constrained and too small for satisfying the huge DNN computation and memory demands. DNN model quantization may come to the rescue. Instead of using 32-bit floating point numbers, much smaller formats can be used, down to 1-bit binary numbers. Although this largely may solve the compute and memory problems, it comes with a huge price, model accuracy reduction. This problem spawned a lot of research into model repair methods, especially for binary neural networks. Heavy quantization triggers a lot of debate; we even see some movements of going back to higher precision using brainfloats. This paper therefore evaluates the trade-off between energy reduction through extreme quantization versus accuracy loss. This evaluation is based on ResNet-I8 with the ImageNet dataset, mapped to a fully programmable architecture with special support for 8-bit and 1-bit deep learning, the BrainTTA. We show that, after applying repair methods, the use of extremely quantized DNNs makes sense. They have superior energy efficiency compared to DNNs based on 8-bit precision of weights and data, while only having a slightly lower accuracy. There is still an accuracy gap, requiring further research, but results are promising. A side effect of the much lower energy requirements of BNNs is that external DRAM becomes more dominant. This certainly requires further attention
How to train accurate BNNs for embedded systems?
Full text linkA key enabler of deploying convolutional neural networks on
resource-constrained embedded systems is the binary neural network (BNN). BNNs
save on memory and simplify computation by binarizing both features and
weights. Unfortunately, binarization is inevitably accompanied by a severe
decrease in accuracy. To reduce the accuracy gap between binary and
full-precision networks, many repair methods have been proposed in the recent
past, which we have classified and put into a single overview in this chapter.
The repair methods are divided into two main branches, training techniques and
network topology changes, which can further be split into smaller categories.
The latter category introduces additional cost (energy consumption or
additional area) for an embedded system, while the former does not. From our
overview, we observe that progress has been made in reducing the accuracy gap,
but BNN papers are not aligned on what repair methods should be used to get
highly accurate BNNs. Therefore, this chapter contains an empirical review that
evaluates the benefits of many repair methods in isolation over the
ResNet-20\&CIFAR10 and ResNet-18\&CIFAR100 benchmarks. We found three repair
categories most beneficial: feature binarizer, feature normalization, and
double residual. Based on this review we discuss future directions and research
opportunities. We sketch the benefit and costs associated with BNNs on embedded
systems because it remains to be seen whether BNNs will be able to close the
accuracy gap while staying highly energy-efficient on resource-constrained
embedded systems
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
Low-and Mixed-Precision Inference Accelerators
No full textWith the surging popularity of edge computing, the need to efficiently perform neural network inference on battery-constrained IoT devices has greatly increased. While algorithmic developments enable neural networks to solve increasingly more complex tasks, the deployment of these networks on edge devices can be problematic due to the stringent energy, latency, and memory requirements. One way to alleviate these requirements is by heavily quantizing the neural network, i.e., lowering the precision of the operands. By taking quantization to the extreme, e.g., by using binary values, new opportunities arise to increase the energy efficiency. Several hardware accelerators exploiting the opportunities of low-precision inference have been created, all aiming at enabling neural network inference at the edge. In this chapter, design choices and their implications on the flexibility and energy efficiency of several accelerators supporting extremely quantized networks are reviewed
POQ:Is There a Pareto-Optimal Quantization Strategy for Deep Neural Networks?
No full textEfficient deployment of deep learning models on resource-constrained devices requires balancing accuracy with energy consumption and/or latency. Quantization is a proven method to achieve this balance by reducing the precision of neural network weights and activations. However, simply changing the precision does not enable direct iso-accuracy and iso-energy comparisons. To address this, we combine a realistic processor energy model with a network filter multiplier that scales the number of channels, thereby enabling such comparisons. This work presents a Pareto-Optimal Quantization (POQ) methodology aimed at mapping a neural network architecture to a specific hardware platform while systematically exploring the design space in between to identify the most effective quantization strategy. Our approach evaluates how different design choices impact the accuracy-energy trade-off. Using detailed energy modeling instead of proxy metrics, our results reveal that 8-bit integer (int8) quantization is Pareto-Optimal for MobileNetV2, providing up to 2.8× energy savings or 10% higher accuracy compared to 16-bit floating-point (fp16). Furthermore, employing high-precision residuals shifts the Pareto frontier, making 4-bit integer (int4) quantization optimal, achieving up to 1.9× additional energy reduction or 2% additional accuracy gains. Moreover, our findings emphasize the role of DRAM energy in certain model configurations and highlight the importance of precise energy modeling. These results reflect the application of our POQ methodology to the practical deployment of energy-efficient deep learning models on constrained hardware.</p
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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