A4 Artikkeli konferenssijulkaisussa
FIST: A Framework to Interleave Spiking Neural Networks on CGRAs




Julkaisun tekijät: Tuan Ngyen, Syed M. A. H. Jafri, Masoud Daneshtalab, Ahmed Hemani, Sergei Dytckov, Juha Plosila, Hannu Tenhunen
Julkaisuvuosi: 2015
Kirjan nimi *: Parallel, Distributed and Network-Based Processing (PDP), 2015 23rd Euromicro International Conference on
Sivujen määrä: 8
ISBN: 978-1-4799-8490-9

Tiivistelmä

Coarse Grained Reconfigurable Architectures


(CGRAs) are emerging as enabling platforms to meet the high


performance demanded by modern embedded applications. In


many application domains (e.g. robotics and cognitive embedded


systems), the CGRAs are required to simultaneously host


processing (e.g. Audio/video acquisition) and estimation (e.g.


audio/video/image recognition) tasks. Recent works have revealed


that the efficiency and scalability of the estimation algorithms


can be significantly improved by using neural networks.


However, existing CGRAs commonly employ homogeneous


processing resources for both the tasks. To realize the best of


both the worlds (conventional processing and neural networks),


we present FIST. FIST allows the processing elements and the


network to dynamically morph into either conventional CGRA


or a neural network, depending on the hosted application. We


have chosen the DRRA as a vehicle to study the feasibility and


overheads of our approach. Synthesis results reveal that the


proposed enhancements incur negligible overheads (4.4% area


and 9.1% power) compared to the original DRRA cell.


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Last updated on 2019-29-01 at 11:45