MAxPy Multi-layer Approximate Computing Python Framework

MAxPy is a framework aimed for simulation and exploration of Approximate Computing techniques in VLSI designs. It is Ptyhon-based, free and open-source.

Check out our documentation!

MAxPy is part of the MAxPy Project.

Installation

pip install MAxPy

Examples

Basic flow

RTL-level

from MAxPy import maxpy
from testbench import testbench_run
circuit = maxpy.AxCircuit(top_name="adder4")
circuit.set_testbench_script(testbench_run)
circuit.rtl2py(target="exact")

Gate-level

from MAxPy import maxpy
from testbench import testbench_run
circuit = maxpy.AxCircuit(top_name="adder4")
circuit.set_testbench_script(testbench_run)
circuit.set_synth_tool("yosys")
circuit.rtl2py(target="exact_yosys")

Parameter exploration

from MAxPy import maxpy
from testbench import testbench_run
circuit = maxpy.AxCircuit(top_name="adder4")
circuit.set_testbench_script(testbench_run)
circuit.set_group("study_no_1")
circuit.set_synth_tool(None)
circuit.set_results_filename("output.csv")
circuit.parameters = {
"[[PARAM_ADDER01]]": ["copyA","copyB", "eta1", "loa", "trunc0", "trunc1"],
"[[PARAM_K]]": ["0", "1", "2", "3"],
}
circuit.rtl2py_param_loop(base="rtl_param")

Probabilist pruning

from MAxPy import maxpy
from MAxPy import probprun
from testbench import testbench_run
circuit = maxpy.AxCircuit(top_name="adder4")
circuit.set_testbench_script(testbench_run)
circuit.set_synth_tool("yosys")
pareto_circuits = circuit.get_pareto_front("area", "mre")
probprun.probprun_loop(circuit, pareto_circuits)