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Ayose Falcón
Researcher at Intel
Publications - 37
Citations - 781
Ayose Falcón is an academic researcher from Intel. The author has contributed to research in topics: Branch predictor & Thread (computing). The author has an hindex of 13, co-authored 37 publications receiving 772 citations. Previous affiliations of Ayose Falcón include Hewlett-Packard & Polytechnic University of Catalonia.
Papers
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Patent
Reconfigurable functional unit and method for artificial neural networks
Frederico Pratas,Ayose Falcón,Marc Lupon,Fernando Latorre,Pedro Lopez,Enric Herrero Abellanas,Georgios Tournavitis +6 more
TL;DR: In this paper, a reconfigurable functional unit and method for ANNs is described, which comprises a polymorphic decoder to generate an index using non-uniform function segmentation; a lookup table (LUT) to store parameters associated with different segments of the non-Uniform Function Segmentation, and a piecewise approximation unit to compute a final result using the parameters provided from the LUT.
Journal ArticleDOI
A latency-conscious SMT branch prediction architecture
TL;DR: This paper proposes several solutions to reduce the effect of branch predictor delay in the performance of SMT processors, and presents an interthread pipelined branch predictor, based on creating a pipeline of interleaved predictions from different threads.
Proceedings ArticleDOI
Shared resource aware scheduling on power-constrained tiled many-core processors
Sudhanshu Shekhar Jha,Wim Heirman,Ayose Falcón,Jordi Tubella,Antonio González,Lieven Eeckhout +5 more
TL;DR: A two-tier hierarchical power management methodology to exploit per-tile voltage regulators and clustered last-level caches is proposed and a novel thread migration layer is included that analyzes threads running on the tiled many-core processor for shared resource sensitivity in tandem with core, cache and frequency adaptation.
Patent
Fine-grain storage interface and method for low power accelerators
Pedro Lopez,Ayose Falcón,Fernando Latorre,Enric Herrero Abellanas,Marc Lupon,Frederico Pratas,Georgios Tournavitis +6 more
TL;DR: In this article, a fine-grained cache access mechanism is described to efficiently perform convolutions in low-power accelerators, where an execution cluster comprises a plurality of processing units, the processing units to perform convolution operations using an input data array and a kernel data array.
Book ChapterDOI
Studying New Ways for Improving Adaptive History Length Branch Predictors
Ayose Falcón,Oliverio J. Santana,Pedro Luis Rodríguez Medina,Enrique Fernández,Alex Ramirez,Mateo Valero +5 more
TL;DR: An in depth evaluation of Dynamic History Length Fitting, a technique that changes the history length of a two-level branch predictor during the execution, trying to adapt to its different phases, shows that new heuristics that minimise both opportunity cost and warm-up cost could outperform significantly current variable history length techniques.