Fixed-point Computation Infrastructure for Ptolemy
Dr. Seehyun Kim---Visiting Scholar, LG Electronics Research Center|
|Advisor:||Edward A. Lee|
In terms of power consumption, hardware cost, and execution speed,
fixed-point arithmetic is often preferable over floating point for
embedded signal processing systems. Algorithms, however, are usually
developed using an idealized arithmetic model, usually approximated by
double-precision floating-point arithmetic on standard workstations. In
practice, one can think of the idealized model as more abstract, and
the fixed-point model as more concrete, closer to the physical world.
Our approach is to make concrete the an abstract specification in the
final phases of the ``algorithm-to-implementation'' design process. In
more adaptive applications, fixed-point properties may be varied in the
field, at run time, for example to conserve power.
In this project, an infrastructure of the fixed-point computation will
be constructed for Ptolemy. Functional fixed-point blocks will be
synthesized automatically from the corresponding floating-point blocks.
Simulation and analysis will then be combined for performance
assessment. Self-tuning systems, which dynamically change their own
parameters to meet varying operational criteria, will also be explored.
For example, the wordlength of a signal or the number of taps of a filter
will be adjusted on-the-fly in order to adapt to a changing criterion,
such as power dissipation or the signal-to-noise ratio.