A collection of classes providing simple RTL specification, simulation, tracing, and testing suitable for teaching and research. Simplicity, usability, clarity, and extendibility rather than performance or optimization is the overarching goal. With PyRTL you can use the full power of Python to describe complex synthesizable digital designs, simulate and test them, and export them to Verilog.


Automatic installation:

pip install pyrtl

PyRTL is listed in PyPI and can be installed with pip or pip3. If the above command fails due to insufficient permissions, you may need to do sudo pip install pyrtl (to install as superuser) or pip install --user pyrtl (to install as a normal user).

PyRTL is tested to work with Python 3.8+.

Design, Simulate, and Inspect in 15 lines

 1import pyrtl
 3a = pyrtl.Input(8,'a')  # input "pins"
 4b = pyrtl.Input(8,'b')
 5q = pyrtl.Output(8,'q')  # output "pins"
 6gt5 = pyrtl.Output(1,'gt5')
 8result = a + b  # makes an 8-bit adder
 9q <<= result  # assigns output of adder to out pin
10gt5 <<= result > 5  # does a comparison, assigns that to different pin
12# simulate and output the resulting waveform to the terminal
13sim = pyrtl.Simulation()
14sim.step_multiple({'a':[0,1,2,3,4], 'b':[2,2,3,3,4]})

After you have PyRTL installed, you should be able to cut and paste the above into a file and run it with Python. The result you should see, drawn right into the terminal, is the output of the simulation. While a great deal of work has gone into making hardware design in PyRTL as friendly a process as possible, please don’t mistake that for a lack of depth. You can just as easily export to Verilog or other hardware formats, view results with your favorite waveform viewer, build hardware transformation passes, run JIT-accelerated simulations, design, test, and even verify hugely complex digital systems, and much more. Most critically of all it is easy to extend with your own approaches to digital hardware development as you find necessary.

Overview of PyRTL

If you are brand new to PyRTL we recommend that you start with going through the Examples of PyRTL Code which will show you most of the core functionality in the context of a complete design.

PyRTL Classes:

Perhaps the most important class to understand is WireVector, which is the basic type from which you build all hardware. If you are coming to PyRTL from Verilog, a WireVector is closest to a multi-bit wire. Every new WireVector builds a set of wires which you can then connect with other WireVector through overloaded operations such as addition or bitwise or. A bunch of other related classes, including Input, Output, Const, and Register are all derived from WireVector. Coupled with MemBlock (and RomBlock), this is all a user needs to create a functional hardware design.

Inheritance diagram of pyrtl.wire.WireVector, pyrtl.wire.Input, pyrtl.wire.Output, pyrtl.wire.Const, pyrtl.wire.Register

After specifying a hardware design, there are then options to simulate your design right in PyRTL, synthesize it down to primitive 1-bit operations, optimize it, and export it to Verilog (along with a testbench),.

To simulate your hardware design one needs to do a simulation, and to view the output we need to capture a “trace”. Simulation is how your hardware is “executed” for the purposes of testing, and three different classes help you do that: Simulation, FastSimulation and CompiledSimulation. All three have almost the same interface and, except for a few debugging cases, can be used interchangeably. Typically one starts with Simulation and then moves up to FastSimulation when performance begins to matter.

Both Simulation and FastSimulation take an instance of SimulationTrace as an argument (or make a new blank one by default), which stores a list of the signals as they are simulated. This trace can then be rendered to the terminal with WaveRenderer, although unless there are some problems with the default configurations, most end users should not need to even be aware of WaveRenderer. The examples describe other ways that the trace may be handled, including extraction as a test bench and export to a VCD file.

When you are building hardware with the WireVector and MemBlock classes, what is really happening under the hood is that those classes are just “sugar” over a core set of primitives and a data structure keeps incrementally updating a graph of those primitives which, when complete, represent the final design. WireVectors connect to “primitives” which connect to other WireVectors and the class that stores a primitive is LogicNet. The class Block is then a wrapper for a set of these LogicNets. Typically a full and complete design is stored in a single Block. The function working_block() will return back the block on which we are implicitly working. When we write hardware transforms we may wish to make a new Block from an old one and augment the information kept with my hardware block and PostSynthBlock is one example of this pattern in action.

Inheritance diagram of pyrtl.core.Block, pyrtl.core.PostSynthBlock

Finally, when things go wrong you may hit on one of two Exceptions, neither of which is likely recoverable automatically (which is why we limited them to only two). The intention is that PyrtlError is intended to capture end user errors such as invalid constant strings and mis-matched bitwidths. In contrast, PyrtlInternalError captures internal invariants and assertions over the core logic graph which should never be hit when constructing designs in the normal ways. If you hit a confusing PyrtlError or any PyrtlInternalError feel free to file an issue.

PyRTL Functionality:

Indices and tables