FIST: A Framework to Interleave Spiking Neural Networks on CGRAs

: Tuan Ngyen, Syed M. A. H. Jafri, Masoud Daneshtalab, Ahmed Hemani, Sergei Dytckov, Juha Plosila, Hannu Tenhunen

: Masoud Daneshtalab, Marco Aldinucci, Ville Leppänen, Johan Lilius, and Mats Brorsson

: Euromicro international conference on parallel, distributed and network-based processing

: 2015

: Parallel, Distributed and Network-Based Processing (PDP), 2015 23rd Euromicro International Conference on

: 751

: 758

: 8

: 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.

tuanedgedetection.pdf