Modeling Capabilities
RF/Wireless
A lot of RF circuits use encoding schemes that are essentially a
sequence of individual tones. From a simulation perspective this is
usually difficult to handle as an analog simulation because it takes a
really small time-step to simulate accurately, but because you are
dealing with tones of multi-cycle duration, a lot of the simulation is
redundant (repeated work). Event driven behavioral models can built in
ParC that use spectral definitions (a complex real array) for signals
on wires, with superposition (via resolution) and array functions to
calculate transmitted spectra. This approach makes RF modeling
considerably less compute intensive than doing it in (say) Verilog-AMS,
and allows whole system verification in reasonable timescales.
Wireless communication can can also be modeled with spectra and use
ParC's signal resolution scheme to model free-space effects by
considering transmitting antennas as drivers into a signal, and
receiving antennas as receivers. Antenna characteristics (e.g.
position, orientation, polarization etc.) are stored in the
driver/receiver classes.
Neural Networks
Unlike HDLs, modules in ParC can be created as desired and wired up
dynamically, writing a model for a neuron is fairly straightforward,
and the threading model supports having billions (see http://en.wikipedia.org/wiki/Neuron).
That flexibilty allows ParC to be used as a base for Artificial
Intelligence (of the kind found in mammals), as well as being capable
of simulating the environment. How one programs that machine/model
remains to be seen, but ParC can be reflective, so maybe with some genetic algorithms we'll get lucky.