Recognition, Mining and Synthesis Moves Comp uters to the Era of Tera

By P. K. Dubey. 2005

152 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b568a32f663511430a2bc94ad02d833ed590a4ce

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Citations in the corpus

Benchmarking modern multiprocessors (2011, background): “It includes not only a number of important RMS applications [24] but also several leading-edge applications from Princeton University, Stanford University, and the opensource domain.”
DaDianNao: A Machine-Learning Supercomputer (2014, background): “Early symptoms of this transformation are Intel calling for a refocus on Recognition, Mining and Synthesis applications in 2005 [14] (which later led to the PARSEC benchmark suite [3]), with Recognition and Mining largely corresponding to machine-learning tasks, or IBM developing the Watson…”
A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads (2010, background): “Are existing application classification taxonomies sufficient to differentiate application characteristics? Several approaches have been proposed for classifying applications based on their memory access and execution patterns, including the Berkeley Dwarves [1] and Intel’s Recognition, Mining and S”
Evaluating MapReduce for Multi-core and Multiprocessor Systems (2007, background): “A recent study by Intel has also concluded that many data-intensive computations can be expressed as sums over data points [9].”
Spin-torque building blocks. (2014, background): “As such they avoid competition with sectors where pure CMOS excels, and open the way for novel types of spintronic accelerators dedicated to specific applications in the field of “Recognition, Mining and Synthesis” [73].”
Scalable-effort classifiers for energy-efficient machine learning (2015, unknown): “In this paper, we focus on an important class of algorithms - machine learning classiﬁers - and show how they can be constructed to scale their computational effort depending on the di culty of the input data, leading to faster ﬃ platforms [1].”
Rodinia: A benchmark suite for heterogeneous computing (2009, background): “It provides workloads in the RMS (Recognition, Mining and Synthesis) [18] and system application domains and represents a wider range of parallelization techniques.”
The PARSEC benchmark suite: Characterization and architectural implications (2008, background): “Emerging Workloads Rapidly increasing processing power is enabling a new class of applications whose computational requirements were beyond the capabilities of the earlier generation of processors [9].”