A4 Refereed article in a conference publication
FIST: A Framework to Interleave Spiking Neural Networks on CGRAs
Authors: Tuan Ngyen, Syed M. A. H. Jafri, Masoud Daneshtalab, Ahmed Hemani, Sergei Dytckov, Juha Plosila, Hannu Tenhunen
Editors: Masoud Daneshtalab, Marco Aldinucci, Ville Leppänen, Johan Lilius, and Mats Brorsson
Conference name: Euromicro international conference on parallel, distributed and network-based processing
Publication year: 2015
Book title : Parallel, Distributed and Network-Based Processing (PDP), 2015 23rd Euromicro International Conference on
First page : 751
Last page: 758
Number of pages: 8
ISBN: 978-1-4799-8490-9
DOI: https://doi.org/10.1109/PDP.2015.60
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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