1,721,835 research outputs found
Exploitation of Digital Twins in Smart Manufacturing
Full text linkIn this paper, we provide a structured summary of initiatives and a survey on the work done in exploitation of DT in Smart Manufacturing (SM) and its very close domains, and offer our points of view in the field
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
Analysis of instruction-level vulnerability to dynamic voltage and temperature variations
No full textVariation in performance and power across manufactured parts and their operating conditions is an accepted reality in aggressive CMOS processes. This paper considers challenges and opportunities in identifying this variation and methods to combat it for improved computing systems. We introduce the notion of instruction-level vulnerability (ILV) to expose variation and its effects to the software stack for use in architectural/compiler optimizations. To compute ILV, we quantify the effect of voltage and temperature variations on the performance and power of a 32-bit, RISC, in-order processor in 65nm TSMC technology at the level of individual instructions. Results show 3.4ns (68FO4) delay variation and 26.7x power variation among instructions, and across extreme corners. Our analysis shows that ILV is not uniform across the instruction set. In fact, ILV data partitions instructions into three equivalence classes. Based on this classification, we show how a low-overhead robustness enhancement techniques can be used to enhance performance by a factor of 1.1x−5.5x
Aging-aware compiler-directed VLIW assignment for GPGPU architectures
No full textNegative bias temperature instability (NBTI) adversely affects the reliability of a processor by introducing new delay-induced faults. However, the effect of these delay variations is not uniformly spread across functional units and instructions: some are affected more (hence less reliable) than others. This paper proposes a NBTI-aware compiler-directed very long instruction word (VLIW) assignment scheme that uniformly distributes the stress of instructions with the aim of minimizing aging of GPGPU architecture without any performance penalty. The proposed solution is an entirely software technique based on static workload characterization and online execution with NBTI monitoring that equalizes the expected lifetime of each processing element by regenerating aging-aware healthy kernels that respond to the specific health state of GPGPU. We demonstrate our approach on AMD Evergreen architecture where iso-throughput executions of the healthy kernels reduce NBTI-induced voltage threshold shift up to 49% (11%) compared to naïve kernel executions, with (without) architectural support for power-gating. The kernel adaption flow takes average of 13 millisecond on a typical host machine thus making it suitable for practical implementation
Hardware optimizations of dense binary hyperdimensional computing: Rematerialization of hypervectors, binarized bundling, and combinational associative memory
Full text linkBrain-inspired hyperdimensional (HD) computing models neural activity patterns of the very size of the brain's circuits with points of a hyperdimensional space, that is, with hypervectors. Hypervectors are Ddimensional (pseudo)random vectors with independent and identically distributed (i.i.d.) components constituting ultra-wide holographic words: D = 10,000 bits, for instance. At its very core, HD computing manipulates a set of seed hypervectors to build composite hypervectors representing objects of interest. It demands memory optimizations with simple operations for an efficient hardware realization. In this article, we propose hardware techniques for optimizations of HD computing, in a synthesizable open-source VHDL library, to enable co-located implementation of both learning and classification tasks on only a small portion of Xilinx UltraScale FPGAs: (1)We propose simple logical operations to rematerialize the hypervectors on the fly rather than loading them from memory. These operations massively reduce the memory footprint by directly computing the composite hypervectors whose individual seed hypervectors do not need to be stored in memory. (2) Bundling a series of hypervectors over time requires a multibit counter per every hypervector component. We instead propose a binarized back-to-back bundling without requiring any counters. This truly enables onchip learning with minimal resources as every hypervector component remains binary over the course of training to avoid otherwise multibit components. (3) For every classification event, an associative memory is in charge of finding the closest match between a set of learned hypervectors and a query hypervector by using a distance metric. This operator is proportional to hypervector dimension (D), and hence may take O(D) cycles per classification event. Accordingly, we significantly improve the throughput of classification by proposing associative memories that steadily reduce the latency of classification to the extreme of a single cycle. (4) We perform a design space exploration incorporating the proposed techniques on FPGAs for a wearable biosignal processing application as a case study. Our techniques achieve up to 2.39× area saving, or 2,337× throughput improvement. The Pareto optimal HD architecture is mapped on only 18,340 configurable logic blocks (CLBs) to learn and classify five hand gestures using four electromyography sensors
Candidate Trapdoor Claw-Free Functions from Group Actions with Applications to Quantum Protocols
No full textA 5 μw Standard Cell Memory-Based Configurable Hyperdimensional Computing Accelerator for Always-on Smart Sensing
Full text linkHyperdimensional computing (HDC) is a brain-inspired computing paradigm-based on high-dimensional holistic representations of vectors. It recently gained attention for embedded smart sensing due to its inherent error-resiliency and suitability to highly parallel hardware implementations. In this work, we propose a programmable all-digital CMOS implementation of a fully autonomous HDC accelerator for always-on classification in energy-constrained sensor nodes. By using energy-efficient standard cell memory (SCM), the design is easily cross-technology mappable. It achieves extremely low power, 5 mu text{W} in typical applications, and an energy efficiency improvement over the state-of-the-art (SoA) digital architectures of up to 3times in post-layout simulations for always-on wearable tasks such as Electromyography (EMG) hand gesture recognition. As part of the accelerator's architecture, we introduce novel hardware-friendly embodiments of common HDC-algorithmic primitives, which results in 3.3times technology scaled area reduction over the SoA, achieving the same accuracy levels in all examined targets. The proposed architecture also has a fully configurable datapath using microcode optimized for HDC stored on an integrated SCM-based configuration memory, making the design 'general-purpose' in terms of HDC algorithm flexibility. This flexibility allows usage of the accelerator across novel HDC tasks, for instance, a newly designed HDC-algorithm for the task of ball bearing fault detection
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
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