def test_feed_through_bench():
G = read.bench(os.path.join(netlists, 'feed_through.bench'))
assert G.nodes['N3']['type'] == 'INPUT'
assert G.nodes['N3_BUFF']['type'] == 'BUFF'
assert G.nodes['N3_BUFF_OUT']['type'] == 'OUTPUT'
assert G.has_edge('N3', 'N3_BUFF')
assert G.has_edge('N3_BUFF', 'N3_BUFF_OUT')
import os
from glob import glob
from pathlib import Path
import read
import write
benchmark_dir = 'Benchmarks'
ISCAS_85_sub_dir = 'ISCAS-85'
ITC_99_sub_dir = 'ITC-99'
verilog_benchmark_dir = 'VerilogBenchmarks'
for sub_dir in [ISCAS_85_sub_dir, ITC_99_sub_dir]:
for file in glob(os.path.join(benchmark_dir, sub_dir, '*.bench')):
graph = read.bench(file)
print(file, graph.size())
dest_dir = os.path.join(verilog_benchmark_dir, sub_dir)
Path(dest_dir).mkdir(parents=True, exist_ok=True)
write.verilog(graph, os.path.join(dest_dir, Path(file).stem))
def test_and_tree_bench():
G_in = read.bench(os.path.join(netlists, 'and_tree.bench'))
write.verilog(G_in, 'temp')
G = read.verilog('temp.v')
assert nx.is_isomorphic(G, G_in, lambda a, b: a['type'] == b['type'])
os.remove('temp.v')
def benchmark_data(benchmark_file_path, format='bench'):
"""
Function to find benchmark data
Arguments:
benchmark_file_path {string} -- Path to the benchmark design file
Keyword Arguments:
format {string} -- The format of the design file (default: {'bench'})
Raises:
ValueError: If format is unknown
Returns:
metrics [dictionary] -- Returns the metrics of the benchmark (in,out,dff,and,or,etc.)
"""
# Read design from file
if format == 'bench':
circuit = read.bench(benchmark_file_path)
elif format == 'verilog':
circuit = read.verilog(benchmark_file_path)
elif format == 'vhdl' or format == 'vhd':
circuit = read.vhdl(benchmark_file_path)
elif format == 'smv':
circuit = read.smv(benchmark_file_path)
else:
raise ValueError(f"Unknown format {format}")
# Find and return metrics
metrics = {}
metrics['input'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'INPUT'
])
metrics['output'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'OUTPUT'
])
metrics['dff'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'DFF'
])
metrics['mux'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'MUX'
])
metrics['and'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'AND'
])
metrics['nand'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'NAND'
])
metrics['or'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'OR'
])
metrics['nor'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'NOR'
])
metrics['xor'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'XOR'
])
metrics['xnor'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'XNOR'
])
metrics['not'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'NOT'
])
metrics['buff'] = len([
signal for signal, sigattr in circuit.nodes.items()
if sigattr['type'] == 'BUFF' or sigattr['type'] == 'BUF'
])
return metrics
def skew(netlist, skew_file_path):
"""
Function to extract inputs, outputs, internal signals and gate types to find probability skew of all signals in the circuit.
Arguments:
netlist {networkx graph} : Graph generated by reading design using the read functions
skew_file_path {string} : Path to file where the skew values are written. CSV file is preferable.
Returns:
None
"""
# Function to calculate skew of output of a gate
def calculate_gate_skew(gate_type, gate_input_skew_list):
"""
Function that returns probability skew of a single gate output w.r.t skew of gate inputs based on the gate type.
Supported gate types - AND, NAND, OR, NOR, NOT, BUFFER, DFF, 2-input XOR, 2-input XNOR, 2-input MUX
:param gate_type: (string) highlights type of gate
:param gate_input_skew_list: (list of numbers) probability skew values corresponding to each gate input
:return: output_skew: (number) probability skew value of gate output
"""
output_skew = 1
gate_type = gate_type.strip()
single_ip = True if len(gate_input_skew_list) < 2 else False
if gate_type == "AND":
if single_ip:
return gate_input_skew_list[0]
for signal_skew in gate_input_skew_list:
output_skew *= (0.5 + signal_skew)
output_skew -= 0.5
elif gate_type == "NAND":
if single_ip:
return -1 * gate_input_skew_list[0]
for signal_skew in gate_input_skew_list:
output_skew *= (0.5 + signal_skew)
output_skew = 0.5 - output_skew
elif gate_type == "OR":
if single_ip:
return gate_input_skew_list[0]
for signal_skew in gate_input_skew_list:
output_skew *= (0.5 - signal_skew)
output_skew = 0.5 - output_skew
elif gate_type == "NOR":
if single_ip:
return -1 * gate_input_skew_list[0]
for signal_skew in gate_input_skew_list:
output_skew *= (0.5 - signal_skew)
output_skew -= 0.5
elif gate_type == "XOR":
if single_ip:
return -0.5
output_skew = -2 * gate_input_skew_list[0] * gate_input_skew_list[1]
elif gate_type == "XNOR":
if single_ip:
return 0.5
output_skew = 2 * gate_input_skew_list[0] * gate_input_skew_list[1]
elif gate_type == "MUX":
if single_ip:
return gate_input_skew_list[0]
output_skew = (
gate_input_skew_list[0] *
(gate_input_skew_list[2] - gate_input_skew_list[1])) + (
(gate_input_skew_list[1] + gate_input_skew_list[2]) / 2)
elif gate_type == "NOT":
output_skew = -1 * gate_input_skew_list[0]
elif gate_type == "BUFF" or gate_type == "BUF" or gate_type == "DFF" or gate_type == "OUTPUT":
output_skew = gate_input_skew_list[0]
else:
print("Error : gate_type is " + gate_type)
output_skew = 0
return output_skew
# Read netlist file if needed
if isinstance(netlist, str):
assert '.' in netlist, f"Design file extension must be provided"
file_type = netlist[netlist.rfind('.') + 1:].strip()
if file_type == 'bench':
netlist = read.bench(netlist)
elif file_type == 'verilog':
netlist = read.verilog(netlist)
else:
raise ValueError(
f"Skew calculation of design type {file_type} is not supported"
)
# Topologically sort the netlist
try:
sorted_nodes = list(nx.topological_sort(netlist))
except (nx.NetworkXError, nx.NetworkXUnfeasible):
raise ValueError("Netlist must be a DAG")
# Find inputs, outputs and store file in list
inputs = [
signal for signal in netlist.nodes
if netlist.nodes[signal]['type'] == 'INPUT'
]
probability_skew = {}
probability_skew = probability_skew.fromkeys(inputs, 0)
# Find probability skew of all nodes in the netlist
for signal in sorted_nodes:
if signal in probability_skew:
continue
fanin = list(netlist.predecessors(signal))
assert all(
net in probability_skew for net in fanin
), f"Skew of some of the fan-in signals of {signal} has not been calculated"
input_skew_list = [probability_skew[net] for net in fanin]
probability_skew[signal] = calculate_gate_skew(
netlist.nodes[signal]['type'], input_skew_list)
assert all(signal in probability_skew for signal in sorted_nodes)
# Write probability values to skew file
if skew_file_path:
with open(skew_file_path, 'w') as skew_file:
skew_file.write("net,Prob0,Prob1\n")
for signal in sorted(probability_skew.keys()):
if netlist.nodes[signal]['type'] == 'OUTPUT':
continue
p0, p1 = 0.5 - probability_skew[signal], probability_skew[
signal] + 0.5
skew_file.write(signal + "," + str(p0) + "," + str(p1) + "\n")
return probability_skew
def test_and_tree_bench():
G = read.bench(os.path.join(netlists, 'and_tree.bench'))
assert G.has_edge('N10', 'N14')
assert G.has_node('N9')
assert G.size() == 15
def test_buff_not_bench():
G_bench = read.bench(os.path.join(netlists, 'buff_not.bench'))
G_verilog = read.verilog(os.path.join(netlists, 'buff_not.v'))
assert G_verilog.nodes['N3']['type'] == 'BUFF'
assert G_verilog.nodes['N4']['type'] == 'NOT'
assert nx.is_isomorphic(G_bench, G_verilog, lambda a, b: a['type'] == b['type'])
def test_feed_through_fan_out_bench():
G = read.bench(os.path.join(netlists, 'feed_through_fan_out.bench'))
for node, node_attr in G.nodes.items():
# ensures no 'type'-less nodes were generated by adding an edge
assert 'type' in node_attr
