553 research outputs found
A Multi-Tier Knowledge Discovery Info-Structure Using Ensemble Techniques
Fokus utama kami ialah untuk mempelajari keujudan peraturan-peraturan yang ditemui
daripada data-data tanpa catatan serta menjana keputusan yang lebih tepat dan
muktamad.
Our terminal focus is to learn rules instances that have been discovered from
unannotated data and generate results with high accuracy
A Multi-Tier Knowledge Discovery Info-Structure Using Ensemble Techniques [QA76.9.D35 S158 2007 f rb].
Fokus utama kami ialah untuk mempelajari keujudan peraturan-peraturan yang ditemui daripada data-data tanpa catatan serta menjana keputusan yang lebih tepat dan muktamad. Ini dilakukan melalui kaedah penghibridan yang merangkumi kedua-dua mekanisma berselia dan tidak berselia.
Our terminal focus is to learn rules instances that have been discovered from unannotated data and generate results with high accuracy. This is done via a hybridized methodology which features both supervised and unsupervised techniques. Unannotated data without prior classification information could now be useful as our research has
brought new insight to knowledge discovery and learning altogether
Fault Tolerance of Self Organizing Maps
International audienceAs the quest for performance confronts resource constraints, major breakthroughs in computing efficiency are expected to benefit from unconventional approaches and new models of computation such as brain-inspired computing. Beyond energy, the growing number of defects in physical substrates is becoming another major constraint that affects the design of computing devices and systems. Neural computing principles remain elusive, yet they are considered as the source of a promising paradigm to achieve fault-tolerant computation. Since the quest for fault tolerance can be translated into scalable and reliable computing systems, hardware design itself and the potential use of faulty circuits have motivated further the investigation on neural networks, which are potentially capable of absorbing some degrees of vulnerability based on their natural properties. In this paper, the fault tolerance properties of Self Organizing Maps (SOMs) are investigated. To asses the intrinsic fault tolerance and considering a general fully parallel digital implementations of SOM, we use the bit-flip fault model to inject faults in registers holding SOM weights. The distortion measure is used to evaluate performance on synthetic datasets and under different fault ratios. Additionally, we evaluate three passive techniques intended to enhance fault tolerance of SOM during training/learning under different scenarios
Fault Tolerance of Self Organizing Maps
International audienceBio-inspired computing principles are considered as a source of promising paradigms for fault-tolerant computation. Among bio-inspired approaches , neural networks are potentially capable of absorbing some degrees of vulnerability based on their natural properties. This calls for attention, since beyond energy, the growing number of defects in physical substrates is now a major constraint that affects the design of computing devices. However, studies have shown that most neural networks cannot be considered intrinsically fault tolerant without a proper design. In this paper, the fault tolerance of Self Organizing Maps (SOMs) is investigated, considering implementations targeted onto field programmable gate arrays (FPGAs), where the bit-flip fault model is employed to inject faults in registers. Quantization and distortion measures are used to evaluate performance on synthetic datasets under different fault ratios. Three passive techniques intended to enhance fault tolerance of SOMs during training/learning are also considered in the evaluation. We also evaluate the influence of technological choices on fault tolerance: sequential or parallel implementation, weight storage policies. Experimental results are analyzed through the evolution of neural prototypes during learning and fault injection. We show that SOMs benefit from an already desirable property: graceful degradation. Moreover, depending on some technological choices, SOMs may become very fault tolerant, and their fault tolerance even improves when weights are stored in an individualized way in the implementation
A Decade of Neural Networks: Practical Applications and Prospects
The Jet Propulsion Laboratory Neural Network Workshop, sponsored by NASA and DOD, brings together sponsoring agencies, active researchers, and the user community to formulate a vision for the next decade of neural network research and application prospects. While the speed and computing power of microprocessors continue to grow at an ever-increasing pace, the demand to intelligently and adaptively deal with the complex, fuzzy, and often ill-defined world around us remains to a large extent unaddressed. Powerful, highly parallel computing paradigms such as neural networks promise to have a major impact in addressing these needs. Papers in the workshop proceedings highlight benefits of neural networks in real-world applications compared to conventional computing techniques. Topics include fault diagnosis, pattern recognition, and multiparameter optimization
Transformer-based models and hardware acceleration analysis in autonomous driving: A survey
Transformer architectures have exhibited promising performance in various
autonomous driving applications in recent years. On the other hand, its
dedicated hardware acceleration on portable computational platforms has become
the next critical step for practical deployment in real autonomous vehicles.
This survey paper provides a comprehensive overview, benchmark, and analysis of
Transformer-based models specifically tailored for autonomous driving tasks
such as lane detection, segmentation, tracking, planning, and decision-making.
We review different architectures for organizing Transformer inputs and
outputs, such as encoder-decoder and encoder-only structures, and explore their
respective advantages and disadvantages. Furthermore, we discuss
Transformer-related operators and their hardware acceleration schemes in depth,
taking into account key factors such as quantization and runtime. We
specifically illustrate the operator level comparison between layers from
convolutional neural network, Swin-Transformer, and Transformer with 4D
encoder. The paper also highlights the challenges, trends, and current insights
in Transformer-based models, addressing their hardware deployment and
acceleration issues within the context of long-term autonomous driving
applications
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