1,720,973 research outputs found
Extending the RISC-V ISA for efficient RNN-based 5G radio resource management
No full textRadio Resource Management in 5G mobile communication is a challenging problem for which Recurrent Neural Networks (RNN) have shown promising results. Accelerating the compute-intensive RNN inference is therefore of utmost importance. Programmable solutions are desirable for effective 5G-RRM coping with the rapidly evolving landscape of RNN variations. In this paper, we investigate RNN inference acceleration by tuning both the instruction set and micro-architecture of a micro-controller-class open-source RISC-V core. We couple HW extensions with software optimizations to achieve an overall improvement in throughput and energy efficiency of 15× and 10× w.r.t. the baseline core on a wide range of RNNs used in various RRM tasks.1
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
RNN-Based Radio Resource Management on Multicore RISC-V Accelerator Architectures
Full text linkRadio resource management (RRM) is critical in 5G mobile communications due to its ubiquity on every radio device and its low latency constraints. The rapidly evolving RRM algorithms with low latency requirements combined with the dense and massive 5G base station deployment ask for an on-the-edge RRM acceleration system with a tradeoff between flexibility, efficiency, and cost-making application-specific instruction-set processors (ASIPs) an optimal choice. In this work, we start from a baseline, simple RISC-V core and introduce instruction extensions coupled with software optimizations for maximizing the throughput of a selected set of recently proposed RRM algorithms based on models using multilayer perceptrons (MLPs) and recurrent neural networks (RNNs). Furthermore, we scale from a single-ASIP to a multi-ASIP acceleration system to further improve RRM throughput. For the single-ASIP system, we demonstrate an energy efficiency of 218 GMAC/s/W and a throughput of 566 MMAC/s corresponding to an improvement of 10x and 10.6x, respectively, over the single-core system with a baseline RV32IMC core. For the multi-ASIP system, we analyze the parallel speedup dependency on the input and output feature map (FM) size for fully connected and LSTM layers, achieving up to 10.2x speedup with 16 cores over a single extended RI5CY core for single LSTM layers and a speedup of 13.8x for a single fully connected layer. On the full RRM benchmark suite, we achieve an average overall speedup of 16.4x, 25.2x, 31.9x, and 38.8x on two, four, eight, and 16 cores, respectively, compared to our single-core RV32IMC baseline implementation
Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine
Full text linkDeep neural networks have achieved impressive results in computer vision and machine learning. Unfortunately, state-of-the-art networks are extremely compute and memory intensive, which makes them unsuitable for mW-devices such as loT end-nodes. Aggressive quantization of these networks dramatically reduces the computation and memory footprint. Binary-weight neural networks (BWNs) follow this trend, pushing weight quantization to the limit. Hardware accelerators for BWNs presented up to now have focused on core efficiency, disregarding I/O bandwidth, and system-level efficiency that are crucial for the deployment of accelerators in ultra-low power devices. We present Hyperdrive: a BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel binary-weight streaming approach, which can he used for an arbitrarily sized convolutional neural network architecture and input resolution by exploiting the natural scalability of the compute units both at chip-level and system-level by arranging Hyperdrive chips systolically in a 2D mesh while processing the entire feature map together in parallel. Hyperdrive achieves 4.3 TOp/s/W system-level efficiency (i.e., including I/Os)-3.1 x higher than state-of-the-art BWN accelerators, even if its core uses resource-intensive FP16 arithmetic for increased robustness
Ara2: Exploring Single- and Multi-Core Vector Processing With an Efficient RVV 1.0 Compliant Open-Source Processor
No full textVector processing is highly effective in boosting processor performance and efficiency for data-parallel workloads. In this paper, we present Ara2, the first fully open-source vector processor to support the RISC-V V 1.0 frozen ISA. We evaluate Ara2's performance on a diverse set of data-parallel kernels for various problem sizes and vector-unit configurations, achieving an average functional-unit utilization of 95% on the most computationally intensive kernels. We pinpoint performance boosters and bottlenecks, including the scalar core, memories, and vector architecture, providing insights into the main vector architecture's performance drivers. Leveraging the openness of the design, we implement Ara2 in a 22nm technology, characterize its PPA metrics on various configurations (2-16 lanes), and analyze its microarchitecture and implementation bottlenecks. Ara2 achieves a state-of-the-art energy efficiency of 37.8 DP-GFLOPS/W (0.8V) and 1.35GHz of clock frequency (critical path: ∼40 FO4 gates). Finally, we explore the performance and energy-efficiency trade-offs of multi-core vector processors: we find that multiple vector cores help overcome the scalar core issue-rate bound that limits short-vector performance. For example, a cluster of eight 2-lane Ara2 (16 FPUs) achieves more than 3x better performance than a 16-lane single-core Ara2 (16 FPUs) when executing a 32x32x32 matrix multiplication, with 1.5x improved energy efficiency
ChewBaccaNN: A flexible 223 TOPS/W BNN accelerator
No full textBinary Neural Networks enable smart IoT devices, as they significantly reduce the required memory footprint and computational complexity while retaining a high network performance and flexibility. This paper presents ChewBaccaNN, a 0.7 mm2 sized binary convolutional neural network (CNN) accelerator designed in GlobalFoundries 22 nm technology. By exploiting efficient data re-use, data buffering, latch-based memories, and voltage scaling, a throughput of 241 GOPS is achieved while consuming just 1.1 mW at 0.4V/154MHz during inference of binary CNNs with up to 7×7 kernels, leading to a peak core energy efficiency of 223 TOPS/W. ChewBaccaNN's flexibility allows to run a much wider range of binary CNNs than other accelerators, drastically improving the accuracy-energy tradeoff beyond what can be captured by the TOPS/W metric. In fact, it can perform CIFAR-10 inference at 86.8% accuracy with merely 1.3 μJ, thus exceeding the accuracy while at the same time lowering the energy cost by 2.8× compared to even the most efficient and much larger analog processing-in-memory devices, while keeping the flexibility of running larger CNNs for higher accuracy when needed. It also runs a binary ResNet-18 trained on the 1000-class ILSVRC dataset and improves the energy efficiency by 4.4× over accelerators of similar flexibility. Furthermore, it can perform inference on a binarized ResNet-18 trained with 8-bases Group-Net to achieve a 67.5% Top-1 accuracy with only 3.0 mJ/frame-at an accuracy drop of merely 1.8% from the full-precision ResNet-18
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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