Multi-layer Approximate Computing Python Framework
Project description
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)
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