An Invited Paper for 1997 Seminar on
Neural Network Applications in Electrical Engineering
Simulation and Synthesis of Artificial Neural Networks
Using Dataflow Models in Ptolemy
Biao Lu (1),
Brian L. Evans (1),
Dejan V. Tosic (2)
(1) Laboratory for Vision Systems,
Department of Electrical and Computer Engineering,
Engineering Science Building,
The University of Texas at Austin,
Austin, TX 78712-1084 USA
(2) School of Electrical Engineering,
University of Belgrade,
Bulevar Revolucije 73,
11000 Belgrade, Yugoslavia
Artificial neural networks (ANNs) are often mixed with digital signal
processing (DSP) to form understanding and interpretation systems.
These systems are heterogeneous in that they contain a variety of
algorithms which may be synthesized onto a variety of software
and hardware technologies.
Because ANN and DSP subsystems are generally data-driven, their computation
and control structure can be unified under a dataflow
model of computation.
In this paper, we demonstrate that ANN and DSP subsystems can be
modeled using dataflow models that yield static implementations
on either sequential or parallel machines.
We map Hopfield, backpropagation, and biological networks to
Homogeneous Synchronous Dataflow (SDF) models.
We combine Boolean Dataflow (BDF) and SDF models to model
Cellular Neural Networks (CNNs).
By modeling DSP operations in SDF, we are free to mix ANNs and DSP subsystems
and still retain efficient simulated and synthesized systems due to the
We give several examples of simulating and synthesizing mixed ANN/DSP
systems using the Ptolemy software environment.
Last Updated 11/07/04.