def instantiate(self, setup):
nnsimTestBench.instantiate(self, setup)
self.trace_generator = ws_PE_trace_generator()
self.traces_stats = 'show'
self.recorder = nnsimRecorder() if self.traces_stats == 'show' else None
self.generated_trace = True
# bookkeeping variables
self.result = []
# ---------------------------------------------------------------------
# IO channels for onchip and offchip communications
# ---------------------------------------------------------------------
# -> input channels for sending the input trace data to the GLBs
# smartbuffer needs channels specified as list format
self.weights_in_chn = Channel()
self.ifmap_chn = Channel()
self.psum_in_chn = Channel()
# -> output channel for reciving calculated psum from the chip
self.psum_out_chn = Channel()
pe_setup = {'weights_data_in_chn': self.weights_in_chn,\
'ifmap_data_in_chn': self.ifmap_chn,\
'psum_data_in_chn': self.psum_in_chn,\
'psum_data_out_chn': self.psum_out_chn}
pe_setup.update(self.arch['PE'])
self.dut = PE(pe_setup)
def feature_extraction(location, csv_file):
csv_f = open(csv_file, "a")
c = csv.writer(csv_f)
for root, dirs, files in os.walk(location):
for f in sorted(files):
try:
file_loc = os.path.join(root, f)
pe = PE(file_loc, name=f, strings=True)
features = pe.fe_values()
c.writerow(features)
csv_f.flush()
except:
print "Invalid file."
pass
def _apply_deadline_cst(self, mapping):
csts = []
PE.init_apps_pe_by_mapping(self.app_list, mapping, self.pe_list)
for idx, app in enumerate(self.app_list):
if config.app_to_cst_dict[idx] == 'Deadline':
csts.append(Deadline(self.app_list, app, self.pe_list))
# if there is deadline constraint
for idx, cst in enumerate(csts):
penalty = cst.constraint_function(mapping)[0]
if penalty != 0:
self._one_bit_flip_local_optimization(mapping, penalty, cst)
return False
return True
def initilize_csv(location, csv_file):
csv_f = open(csv_file, "a")
c = csv.writer(csv_f)
for root, dirs, files in os.walk(location):
for f in sorted(files):
try:
file_loc = os.path.join(root, f)
pe = PE(file_loc, name=f, strings=True)
header = pe.fe_methods()
c.writerow(header)
csv_f.flush()
csv_f.close()
return
except:
print "Invalid file."
pass
def _apply_cpu_util_cst(self, mapping):
if config.CPU_UTILIZATION != 100:
PE.init_apps_pe_by_mapping(self.app_list, mapping, self.pe_list)
cpu_util = CPU_utilization(self.app_list, None, self.pe_list)
while cpu_util.constraint_function(mapping)[0] != 0: # (penalty, util)
for idx in range(len(mapping)):
l = self.layer_list[idx]
if not l.is_start_node and not l.is_end_node and l.pe.type == PEType.CPU:
best = float("inf")
best_pe_idx = None
for pe in self.pe_list:
target = l.time_list[pe.get_idx()]
if target < best:
best = target
best_pe_idx = pe.get_idx()
mapping[idx] = best_pe_idx
def calculate_fitness(self, mapping):
PE.init_apps_pe_by_mapping(self.app_list, mapping, self.pe_list)
objs = []
for obj in self.objs:
obj_temp = obj.objective_function(mapping)
objs.append(obj_temp[0])
for idx, cst in enumerate(self.csts):
if cst is not None and isinstance(cst, Deadline):
cst_temp = cst.constraint_function(mapping)
objs[idx] += cst_temp[0]
elif cst is not None:
cst_temp = cst.constraint_function(mapping)
objs = [v + cst_temp[0] for v in objs]
return tuple(objs)
def _move_large2small(self, mapping):
large = -float("inf")
small = float("inf")
large_pe = None
small_pe = None
PE.init_apps_pe_by_mapping(self.app_list, mapping, self.pe_list)
for idx in range(0, self.num_pe):
if self.cur_map_stat_per_pe[self.pe_list[idx].name] > large:
large = self.cur_map_stat_per_pe[self.pe_list[idx].name]
large_pe = self.pe_list[idx]
if self.cur_map_stat_per_pe[self.pe_list[idx].name] < small:
small = self.cur_map_stat_per_pe[self.pe_list[idx].name]
small_pe = self.pe_list[idx]
for idx in range(0, self.num_layer):
if mapping[idx] == large_pe.get_idx():
mapping[idx] = small_pe.get_idx()
self.cur_map_stat_per_pe[large_pe.name] -= self.layer_list[idx].time_list[large_pe.get_idx()]
self.cur_map_stat_per_pe[small_pe.name] += self.layer_list[idx].time_list[small_pe.get_idx()]
break
def __init__(self, project, sample):
from pe import PE
self.project = project
self.sample = sample
self.pe = PE(sample.file)
import os
from pe import PE
import distorm3
# distorm3 things
offset = 0
dt = distorm3.Decode32Bits
f = open('print.exe', 'rb')
o = PE(f)
# distorm3
f.seek(o.rva2ofs(o.entrypoint))
code = f.read()
iterable = distorm3.DecomposeGenerator(offset, code, dt, \
distorm3.DF_RETURN_FC_ONLY | distorm3.DF_STOP_ON_FLOW_CONTROL)
print iterable.next()
offset = workRva
# read the next instruction
code = f.read()
iterable = distorm3.DecomposeGenerator(offset, code, dt, \
distorm3.DF_RETURN_FC_ONLY | distorm3.DF_STOP_ON_FLOW_CONTROL)
inst = iterable.next()
# if we've encountered a loop exit
if hasAddr(inst.address):
print 'Found a loop!', hex(inst.address), inst, inst.flowControl
return
workRva = inst.address
if __name__ == '__main__':
f = PE(open('print.exe', 'rb'))
print 'ImageBase', f.imagebase
print 'entrypoint ofs', hex(f.rva2ofs(f.entrypoint))
getExterns(f)
# some datastructure of interest
workQ = collections.deque()
# distorm3
dt = distorm3.Decode32Bits
# inst1
f.seek(f.rva2ofs(f.entrypoint))
code = f.read()
workQ.append(f.entrypoint)
class Crafter(object):
""" this class craft the objects and store them in the database returning
dicts (or db entries) """
def __init__(self, project, sample):
from pe import PE
self.project = project
self.sample = sample
self.pe = PE(sample.file)
def mutation_sectionchar_rwx(self):
self.pe.modify_section_characteristics_rwx()
def mutation_sectionname_random(self):
self.pe.modify_section_names(rand=True)
def mutation_sectionname_randomdot(self):
self.pe.modify_section_names(rand=True, with_dot=True)
def mutation_sectionname_infer(self):
self.pe.modify_section_names()
def mutation_code_entryret(self):
self.pe.patch_code(va=self.pe.OPTIONAL_HEADER.AddressOfEntryPoint)
def update_checksum(self):
self.pe.update_checksum()
def _craft_all(self):
results = {}
for mutate_funcs in self.mutations:
name = os.path.join(self.outdir, self.filename)
mut_names = '_'.join([n for n, f in mutate_funcs if f is not None])
if mut_names:
name += '_' + mut_names
for n, f in mutate_funcs:
if f is not None:
f(self)
#verify if checksum is called it should be last
if 'checksum' in name and name.index(
'checksum') != len(name) - len('checksum'):
raise Exception("Checksum was not the last mutation called")
self.workpe.write(name)
with open(name) as fin:
sha = sha256(fin.read()).hexdigest()
results[sha] = [n for n, f in mutate_funcs] # data about mutation!
return results
def instantiate(self, arr_y,
input_chn, output_chn,
block_size, num_nonzero,
ifmap_glb_depth, psum_glb_depth, weight_glb_depth):
# PE static configuration (immutable)
self.name = 'chip'
#self.arr_x = arr_x
self.arr_y = arr_y
self.block_size = block_size
self.num_nonzero = num_nonzero
self.stat_type = 'show'
# Instantiate DRAM IO channels
self.input_chn = input_chn
self.output_chn = output_chn
# Instantiate input deserializer and output serializer
self.ifmap_wr_chn = Channel()
self.psum_wr_chn = Channel()
self.weights_wr_chn = Channel()
self.deserializer = InputDeserializer(self.input_chn, self.ifmap_wr_chn,
self.weights_wr_chn, self.psum_wr_chn, arr_y,
block_size, num_nonzero)
self.psum_output_chn = Channel()
self.serializer = OutputSerializer(self.output_chn, self.psum_output_chn)
# Instantiate GLB and GLB channels
self.ifmap_rd_chn = Channel(3)
#self.ifmap_glb = IFMapGLB(self.ifmap_wr_chn, self.ifmap_rd_chn, arr_y,
# ifmap_glb_depth, block_size, num_nonzero)
self.psum_rd_chn = Channel(3)
self.psum_noc_wr_chn = Channel()
self.psum_glb = PSumGLB(self.psum_wr_chn, self.psum_noc_wr_chn, self.psum_rd_chn,
psum_glb_depth, block_size, num_nonzero)
self.weights_rd_chn = Channel()
#self.weights_glb = WeightsGLB(self.weights_wr_chn, self.weights_rd_chn, weight_glb_depth, block_size)
self.ifmap_weights_glb = IFMapWeightsGLB(self.ifmap_wr_chn, self.ifmap_rd_chn,\
self.weights_wr_chn, self.weights_rd_chn, arr_y, ifmap_glb_depth,\
weight_glb_depth, block_size, num_nonzero)
# PE Array and local channel declaration
self.pe_array = ModuleList()
self.pe_ifmap_chns = ModuleList()
self.pe_filter_chns = ModuleList()
self.pe_psum_in_chns = ModuleList()
self.pe_psum_out_chns = ModuleList()
# Actual array instantiation
for y in range(self.arr_y):
self.pe_array.append(ModuleList())
self.pe_ifmap_chns.append(ModuleList())
self.pe_filter_chns.append(ModuleList())
self.pe_psum_in_chns.append(ModuleList())
self.pe_psum_out_chns.append(ModuleList())
for x in range(1):
self.pe_ifmap_chns[y].append(Channel(32))
self.pe_filter_chns[y].append(Channel(32))
self.pe_psum_in_chns[y].append(Channel(32))
self.pe_psum_out_chns[y].append(Channel(32))
self.pe_array[y].append(
PE(x, y,
self.pe_ifmap_chns[y][x],
self.pe_filter_chns[y][x],
self.pe_psum_in_chns[y][x],
self.pe_psum_out_chns[y][x]
)
)
# Setup NoC to deliver weights, ifmaps and psums
self.filter_noc = WeightsNoC(self.weights_rd_chn, self.pe_filter_chns, block_size)
self.ifmap_noc = IFMapNoC(self.ifmap_rd_chn, self.pe_ifmap_chns)
self.psum_rd_noc = PSumRdNoC(self.psum_rd_chn, self.pe_psum_in_chns, self.arr_y, block_size)
self.psum_wr_noc = PSumWrNoC(self.pe_psum_out_chns, self.psum_noc_wr_chn, self.psum_output_chn, self.arr_y, block_size)
import os
import sys
from pe import PE
import distorm3
encountered = list()
def hasAddr(addr):
for r in encountered:
if addr in r:
return True
return False
if __name__ == '__main__':
pe = PE(open('print.exe', 'rb'))
print 'ImageBase', pe.imagebase
print 'entrypoint ofs', hex(pe.rva2ofs(pe.entrypoint))
# distorm3
dt = distorm3.Decode32Bits
# inst1
pe.seek(pe.rva2ofs(pe.entrypoint))
code = pe.read()
offset = pe.entrypoint
iterable = distorm3.DecomposeGenerator(offset, code, dt, \
distorm3.DF_RETURN_FC_ONLY | distorm3.DF_STOP_ON_FLOW_CONTROL)
inst = iterable.next()
# add what we've encountered
def build_PE(in_dim,
out_dim,
name='BNN',
hidden_dims=(200, 200, 200),
num_networks=7,
num_elites=5,
loss='MSPE',
activation='swish',
output_activation=None,
decay=1e-4,
lr=1e-3,
lr_decay=None,
decay_steps=None,
use_scaler_in=False,
use_scaler_out=False,
clip_loss=False,
kl_cliprange=0.1,
max_logvar=.5,
min_logvar=-6,
session=None):
"""
Constructs a tf probabilistic ensemble model.
Args:
loss: Choose from 'MSPE', 'NLL', 'MSE', 'Huber', or 'CE'.
choosing MSPE or NLL will construct a model with variance output
"""
print('[PE] dim in / out: {} / {} | Hidden dim: {}'.format(
in_dim, out_dim, hidden_dims))
#print('[ BNN ] Input Layer dim: {} | Output Layer dim: {} '.format(obs_dim_in+act_dim+prior_dim, obs_dim_out+rew_dim))
params = {
'name': name,
'loss': loss,
'num_networks': num_networks,
'num_elites': num_elites,
'sess': session,
'use_scaler_in': use_scaler_in,
'use_scaler_out': use_scaler_out,
'clip_loss': clip_loss,
'kl_cliprange': kl_cliprange,
'max_logvar': max_logvar,
'min_logvar': min_logvar,
}
model = PE(params)
model.add(
FC(hidden_dims[0],
input_dim=in_dim,
activation=activation,
weight_decay=decay / 4)) # def dec: 0.000025))
for hidden_dim in hidden_dims[1:]:
model.add(FC(hidden_dim, activation=activation,
weight_decay=decay / 2)) # def dec: 0.00005))
model.add(FC(out_dim, activation=output_activation,
weight_decay=decay)) # def dec: 0.0001
opt_params = {
"learning_rate": lr
} if lr_decay is None else {
"learning_rate": lr,
"learning_rate_decay": lr_decay,
"decay_steps": decay_steps
}
model.finalize(tf.train.AdamOptimizer, opt_params, lr_decay=lr_decay)
total_parameters = 0
for variable in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=name):
# shape is an array of tf.Dimension
shape = variable.get_shape()
variable_parameters = 1
for dim in shape:
variable_parameters *= dim.value
total_parameters += variable_parameters
print('[ Probabilistic Ensemble ] Total trainable Parameteres: {} '.format(
total_parameters))
return model
def pack(self, stub_path, output_path, input_file):
f = open(stub_path, 'rb')
stub_file = f.read()
f.close()
# encrypt the input
key_str = ''
for i in range(4):
key_str += chr(random.randint(1, 0xFF))
input_encrypted = self.xor_long(input_file, key_str)
input_file = key_str + input_encrypted
# increment the number of sections
dos_header = PE.unpack(PE.IMAGE_DOS_HEADER, stub_file)
peHeader_offset = dos_header['e_lfanew']
numberOfSections_offset = peHeader_offset + 0x6
numberOfSections = struct.unpack(
'<H',
stub_file[numberOfSections_offset:numberOfSections_offset + 2])[0]
numberOfSections = numberOfSections + 1
stub_file = stub_file[:numberOfSections_offset] + struct.pack(
'<H', numberOfSections) + stub_file[numberOfSections_offset + 2:]
# add the section header
sectionHeader_offset = peHeader_offset + 0xF8
last_section_offset = sectionHeader_offset + (
(numberOfSections - 2) * 0x28) # last section
print 'last section: {0:x}'.format(last_section_offset)
last_section = PE.unpack(PE.IMAGE_SECTION_HEADER,
stub_file[last_section_offset:])
new_section_header_offset = last_section_offset + 0x28
new_section = PE.structDictionary(PE.IMAGE_SECTION_HEADER)
new_section['Name'] = '.rsrc'
new_section['Misc_VirtualSize'] = len(input_file)
new_section['VirtualAddress'] = PE.align_value(
last_section['VirtualAddress'] + last_section['SizeOfRawData'],
0x1000)
new_section['SizeOfRawData'] = PE.align_value(len(input_file), 0x200)
new_section['PointerToRawData'] = PE.align_value(len(stub_file), 0x200)
new_section[
'Characteristics'] = PE.IMAGE_SCN_CNT_INITIALIZED_DATA | PE.IMAGE_SCN_MEM_READ | PE.IMAGE_SCN_MEM_WRITE | PE.IMAGE_SCN_ALIGN_4BYTES
stub_file = stub_file[:new_section_header_offset] + PE.packDictionary(
PE.IMAGE_SECTION_HEADER,
new_section) + stub_file[new_section_header_offset + 0x28:]
# section jam
stub_file += '\x00' * (new_section['PointerToRawData'] -
len(stub_file))
# append the section
stub_file += input_file
padding = new_section['SizeOfRawData'] - len(input_file)
stub_file += '\x00' * padding
# increment SizeOfImage
optionalHeader_offset = peHeader_offset + 0x18
optionalHeader_size = PE.structSize(PE.IMAGE_OPTIONAL_HEADER)
optional_header = PE.unpack(PE.IMAGE_OPTIONAL_HEADER,
stub_file[optionalHeader_offset:])
optional_header['SizeOfImage'] = PE.align_value(
new_section['VirtualAddress'] + new_section['SizeOfRawData'],
0x1000)
stub_file = stub_file[:optionalHeader_offset] + PE.packDictionary(
PE.IMAGE_OPTIONAL_HEADER,
optional_header) + stub_file[optionalHeader_offset +
optionalHeader_size:]
# save the file
f = open(output_path, 'wb')
f.write(stub_file)
f.close()
code += asm_seh_function
offset_code += len(asm_seh_function)
# AntiEmulator
asm_anti_emulator = stub.asm_anti_emulator(meta_engine)
meta_engine.obfs_block(asm_anti_emulator, obfs_level, obfs_variation)
asm_anti_emulator = asm_anti_emulator.assemble()
stub.add_function('AntiEmulator', offset_code)
code += asm_anti_emulator
offset_code += len(asm_anti_emulator)
# Program
entry_point = base_offset_code + offset_code
print 'Entry point: ', hex(entry_point)
program_size = PE.align_value(
base_offset_data + len(data),
0x1000) # this can be bad... @TODO update the push
asm_program = stub.asm_program(meta_engine, base_offset_code, program_size,
entry_point)
meta_engine.obfs_block(asm_program, obfs_level, obfs_variation, 1)
asm_program = asm_program.assemble()
code += asm_program
offset_code += len(asm_program)
(l, h) = hist(data + code)
print 'New entropy:', round(entropy(h, l), 2)
########################################################################
# Make the IAT
########################################################################
class ws_PE_tb(nnsimTestBench):
''' Testbench specifically for weight stationary PE_ARARRY + GLB + NoC testing '''
def instantiate(self, setup):
nnsimTestBench.instantiate(self, setup)
self.trace_generator = ws_PE_trace_generator()
self.traces_stats = 'show'
self.recorder = nnsimRecorder() if self.traces_stats == 'show' else None
self.generated_trace = True
# bookkeeping variables
self.result = []
# ---------------------------------------------------------------------
# IO channels for onchip and offchip communications
# ---------------------------------------------------------------------
# -> input channels for sending the input trace data to the GLBs
# smartbuffer needs channels specified as list format
self.weights_in_chn = Channel()
self.ifmap_chn = Channel()
self.psum_in_chn = Channel()
# -> output channel for reciving calculated psum from the chip
self.psum_out_chn = Channel()
pe_setup = {'weights_data_in_chn': self.weights_in_chn,\
'ifmap_data_in_chn': self.ifmap_chn,\
'psum_data_in_chn': self.psum_in_chn,\
'psum_data_out_chn': self.psum_out_chn}
pe_setup.update(self.arch['PE'])
self.dut = PE(pe_setup)
def configure(self):
# ====================================================================================================
# 1. check if the setup meets the constraints, if not met, program will exit
# 2. apply paddings to input channels if total input channels not divisible by input channel mapping
# ====================================================================================================
check_constriant(self.mapping, self.shape, self.arch, self.ifmap_data, self.weights_data, self.bias_data)
# ====================================================================================================
print('>> Generating input/output traces ')
# configure trace generator
trace_gen_config = { 'mapping': self.mapping,\
'shape': self.shape,\
'ifmap_data': self.ifmap_data,\
'weights_data': self.weights_data,\
'bias_data': self.bias_data}
self.trace_generator.configure(trace_gen_config)
traces = self.trace_generator.generate_trace()
# configure input traces
self.weights_itrace = traces['weights']
self.ifmap_itrace = traces['ifmap']
self.psum_itrace = traces['psum_in']
self.psum_otrace = traces['psum_out']
print('>> Configuring the testbech and unit under test ')
# bookkeeping indexes used to step through the generated traces cycle by cycle
self.weights_itrace_idx = 0
self.ifmap_itrace_idx = 0
self.psum_itrace_idx = 0
#===========================================================================
# PE configuration
#===========================================================================
# configure the PE for specific workload and define the output channel
lm_sram_map = {'WeightsSp': [{'drain': 0, 'fill': 1}]}
# weight scratchpad (depth = 1) config info
buffer_config = {'LM': [{'base': 0, 'bound': self.arch['PE']['wsp_depth']-1}],\
'lm_sram_map':lm_sram_map['WeightsSp'].copy() }
shape_mapping_info = {'shape': self.shape.copy(), 'mapping': self.mapping.copy()}
config = { 'WeightsSp': buffer_config,\
'shape_mapping_info': shape_mapping_info,\
'clk_gated': False}
self.dut.configure(config)
print('----------------------------------------------------------------------------------')
print(' Simulation starting ...')
print('----------------------------------------------------------------------------------')
def tick(self):
# ----------------------------------------------------------------------------------------
# Send Traces into the Designs input channels, cycle by cycle
# ----------------------------------------------------------------------------------------
# ------------------------- weights input trace ----------------------------------------------
if self.weights_in_chn.vacancy():
if self.weights_itrace_idx < len(self.weights_itrace):
if not np.isnan(self.weights_itrace[self.weights_itrace_idx]):
weight_data_to_send = [ self.weights_itrace[self.weights_itrace_idx]]
self.weights_in_chn.push(weight_data_to_send)
self.weights_itrace_idx += 1
else:
self.weights_done = True
# ------------------------- ifmap input trace ------------------------------------------------
if self.ifmap_chn.vacancy():
if self.ifmap_itrace_idx < len(self.ifmap_itrace):
if not np.isnan(self.ifmap_itrace[self.ifmap_itrace_idx]):
ifmap_data_to_send = [ self.ifmap_itrace[self.ifmap_itrace_idx]]
self.ifmap_chn.push(ifmap_data_to_send)
self.ifmap_itrace_idx += 1
else:
self.ifmap_done = True
if self.psum_in_chn.vacancy():
if self.psum_itrace_idx < len(self.psum_itrace):
if not np.isnan(self.psum_itrace[self.psum_itrace_idx]):
psum_data_to_send = [ self.psum_itrace[self.psum_itrace_idx]]
self.psum_in_chn.push(psum_data_to_send)
self.psum_itrace_idx += 1
else:
self.psum_done = True
# -------------------------------------------------------------------------------------------
# Collect output from the design's output channel
# -------------------------------------------------------------------------------------------
if self.psum_out_chn.valid():
psum_out = self.psum_out_chn.pop()
for idx in range(len(psum_out)):
p = psum_out[idx]
self.result.append(p)
# print('-->', len(self.result), 'calculated:', p, 'reference:', self.psum_otrace[len(self.result)-1], 'total:',len(self.psum_otrace) )
# -------------------------------------------------------------------------------------------
# Compare the design's output with the generated output traces to validate functionality
# -------------------------------------------------------------------------------------------
if len(self.result) == len(self.psum_otrace):
if self.result == self.psum_otrace:
raise Finish('Success')
else:
print('Failed')
print('---------ofmap-----------------')
print(self.result)
print('----------ofmap_trace------------')
print(self.psum_otrace)
print('---------difference------------')
difference = self.result
for i in range(len(difference)):
difference[i] = self.result[i] - self.psum_otrace[i]
print(difference)
raise Finish()
def instantiate(self, setup):
self.pe_array_row = setup['pe_array'][0]
self.pe_array_col = setup['pe_array'][1]
# ---------------------------------------------------------------------
# io data b/w offchip
# ---------------------------------------------------------------------
self.weights_in_chns = setup['io_chns']['weights']
self.ifmap_in_chns = setup['io_chns']['ifmap']
self.psum_in_chns = setup['io_chns']['psum_in']
self.psum_out_chn = setup['io_chns']['psum_out']
# ---------------------------------------------------------------------
# onchip channels
# ---------------------------------------------------------------------
# >> buffer output channels
self.weights_out_chns = ModuleList(Channel())
self.ifmap_out_chns = ModuleList(Channel())
self.ifmap_out_chns = ModuleList(Channel())
self.psum_out_chns = ModuleList(Channel())
# >> update data to buffer
self.psum_update_chns = ModuleList(Channel())
# ===================================================================
# GLBs
# ===================================================================
# ------------------------ WEIGHTS -------------------------------
weights_glb_setup = {'fill_data_ichns': self.weights_in_chns,\
'drain_data_ochns': self.weights_out_chns, \
'num_logical_managers': 1,\
'SRAM': {\
'depth': setup['depth']['WeightsGLB'],\
'width': setup['width']['WeightsGLB'],\
'data_width': setup['data_width']['WeightsGLB'],\
'nbanks': setup['nbanks']['WeightsGLB'],\
'nports': setup['nports']['WeightsGLB'],\
'port_type': setup['port_type']['WeightsGLB'],\
},\
'debug': ' WeightsGLB'}
self.weights_glb = WeightsGLB(weights_glb_setup)
# ------------------------ IFMAP -------------------------------
ifmap_glb_setup = {'fill_data_ichns': self.ifmap_in_chns,\
'drain_data_ochns': self.ifmap_out_chns, \
'num_logical_managers': 1,\
'SRAM': {\
'depth': setup['depth']['IFmapGLB'],\
'width': setup['width']['IFmapGLB'],\
'data_width': setup['data_width']['IFmapGLB'],\
'nbanks': setup['nbanks']['IFmapGLB'],\
'nports': setup['nports']['IFmapGLB'],\
'port_type': setup['port_type']['IFmapGLB'],\
},\
'debug': ' IFmapGLB'}
self.ifmap_glb = IFmapGLB(ifmap_glb_setup)
# ------------------------ PSUM -------------------------------
psum_glb_setup = {'fill_data_ichns': self.psum_in_chns,\
'update_data_ichns': self.psum_update_chns,\
'drain_data_ochns': self.psum_out_chns, \
'num_logical_managers': 1,\
'SRAM': {\
'depth': setup['depth']['PsumGLB'],\
'width': setup['width']['PsumGLB'],\
'data_width': setup['data_width']['PsumGLB'],\
'nbanks': setup['nbanks']['PsumGLB'],\
'nports': setup['nports']['PsumGLB'],\
'port_type': setup['port_type']['PsumGLB'],\
},\
'debug': ' PsumGLB'}
self.psum_glb = PsumGLB(psum_glb_setup)
# ===================================================================
# PE Array Channels
# ===================================================================
self.ifmap_pe_data_chns = ModuleList()
self.weights_pe_data_chns = ModuleList()
self.psum_data_chns = ModuleList()
self.pe_data_chns = {'ifmap': self.ifmap_pe_data_chns,\
'weights': self.weights_pe_data_chns,\
'psum': self.psum_data_chns}
for pe_row in range(self.pe_array_row):
for chn_type, chn_row in self.pe_data_chns.items():
chn_row.append(ModuleList())
for pe_col in range(self.pe_array_col):
for chn_type, chn_col in self.pe_data_chns.items():
chn_col[pe_row].append(Channel())
self.psum_data_chns.append(ModuleList())
for pe_col in range(self.pe_array_col):
self.psum_data_chns[-1].append(Channel())
# ===================================================================
# Destination Calculators for NoC
# ===================================================================
self.ifmap_NoC_destination_chn = ModuleList(Channel())
ifmap_NoC_destination_calculator_setup = \
{'out_chn': self.ifmap_NoC_destination_chn[0],\
'out_channel_width': 1,\
'debug': 'IFmapNoCDestCalc'}
self.ifmap_NoC_destination_calculator = IFmapNoCDestCalc(
ifmap_NoC_destination_calculator_setup)
self.weights_NoC_destination_chn = ModuleList(Channel())
weights_NoC_desitnation_calculator_setup = \
{'out_chn': self.weights_NoC_destination_chn[0],\
'out_channel_width': 1,\
'debug': 'WeightsNoCDestCalc'}
self.weights_NoC_destionation_calculator = WeightsNoCDestCalc(
weights_NoC_desitnation_calculator_setup)
self.psum_in_NoC_destination_chn = ModuleList(Channel())
psum_in_NoC_desitnation_calculator_setup = \
{'out_chn': self.psum_in_NoC_destination_chn[0],\
'out_channel_width': 1,\
'debug': 'PsumInNoCDestCalc'}
self.psum_in_NoC_destionation_calculator = PsumInNoCDestCalc(
psum_in_NoC_desitnation_calculator_setup)
# ===================================================================
# NoCs
# ===================================================================
# weights serializer 4:1
self.weights_serializered_data_chn = Channel()
weights_serializer_setup = {'in_chn': self.weights_out_chns[0],\
'out_chn': self.weights_serializered_data_chn,\
'ratio': setup['weights_seri_ratio'],\
'debug': 'weights_serialzer'}
self.weights_serializer = WeightsSerializer(weights_serializer_setup)
# Weights NoC: weightGLB -> PEs
weights_noc_setup = {'rd_chns': self.pe_data_chns['weights'],\
'wr_chns': self.weights_serializered_data_chn,\
'dest_chns': self.weights_NoC_destination_chn,\
'debug': 'WeightsNoC'}
self.weightsNoC = WeightsNoC(weights_noc_setup)
# -------------- IFmap NoC: IfmapGLB -> PEs -----------------------
# ifmap serializer 4:1
self.ifmap_serialized_data_chn = Channel()
ifmap_serializer_setup = {'in_chn': self.ifmap_out_chns[0],\
'out_chn': self.ifmap_serialized_data_chn,\
'ratio': setup['ifmap_seri_ratio'],\
'debug': 'ifmap_serializer'}
self.ifmap_serilizer = IfmapSerializer(ifmap_serializer_setup)
# ifmap NoC
ifmap_noc_wr_chns = ModuleList()
ifmap_noc_wr_chns.append(self.ifmap_serialized_data_chn)
ifmap_noc_setup = {'rd_chns': self.pe_data_chns['ifmap'],\
'wr_chns': ifmap_noc_wr_chns,\
'dest_chns': self.ifmap_NoC_destination_chn,\
'debug': 'IFmapNoC'}
self.ifmapNoC = IFMapNoC(ifmap_noc_setup)
# ---------------------------------------------------------------------
# -------------- PsumRd NoC: Psum GLB -> PEs ----------------------
# psum serializer 4:1
self.psum_serialized_data_chn = Channel()
psum_serializer_setup = {'in_chn': self.psum_out_chns[0],\
'out_chn': self.psum_serialized_data_chn,\
'ratio': setup['psum_seri_ratio'],\
'debug': 'psum_serialzer'}
self.psum_serializer = PsumSerializer(psum_serializer_setup)
# psum read noc
pe_noc_wr_chn = ModuleList(self.psum_serialized_data_chn)
psum_rd_noc_setup = {'rd_chns': self.psum_data_chns,\
'wr_chns': pe_noc_wr_chn,\
'dest_chns': self.psum_in_NoC_destination_chn,\
'debug': 'PsumRdNoC'}
self.psumRdNoC = PsumRdNoC(psum_rd_noc_setup)
# ---------------------------------------------------------------------
# -------------- PsumWrNoC: PEs -> ifmapPsum GLB ---------------------
self.psum_out_noc_rd_chns = ModuleList()
self.psum_out_noc_rd_chns.append(
self.psum_update_chns[0]) # write back to GLB
self.psum_out_noc_rd_chns.append(self.psum_out_chn) # write offchip
psum_wr_noc_setup = {'rd_chns': self.psum_out_noc_rd_chns,\
'wr_chns': self.psum_data_chns,\
'debug': 'PsumWrNoC'}
self.psumWrNoC = PsumWrNoC(psum_wr_noc_setup)
# ===================================================================
# PE Array
# ===================================================================
# general setup of a PE
PE_setup = setup['PE']
self.PE = ModuleList()
for pe_row in range(self.pe_array_row):
self.PE.append(ModuleList())
for pe_col in range(self.pe_array_col):
# PE specific setup
PE_setup['row'] = pe_row
PE_setup['col'] = pe_col
PE_setup['weights_data_in_chn'] = self.weights_pe_data_chns[
pe_row][pe_col]
PE_setup['ifmap_data_in_chn'] = self.ifmap_pe_data_chns[
pe_row][pe_col]
PE_setup['psum_data_in_chn'] = self.psum_data_chns[pe_row][
pe_col]
PE_setup['psum_data_out_chn'] = self.psum_data_chns[pe_row +
1][pe_col]
self.PE[pe_row].append(PE(PE_setup))
def pack(self):
# names table
number_of_dll = len(self.functions)
names_table_base = self.base_address + PE.structSize(PE.IMAGE_IMPORT_DESCRIPTOR)*(number_of_dll + 1) # for the last null value
names_table = ''
names_table_offset = {}
offset = names_table_base
tmp = ''
for (dll_name, functions) in self.functions.items(): # for each dll
names_table_offset[dll_name] = offset
tmp = dll_name + '\x00'
names_table += tmp
offset += len(tmp)
for function_name in functions: # for each function in this dll
names_table_offset[function_name] = offset
tmp = '\x00\x00' + function_name + '\x00'
names_table += tmp
offset += len(tmp)
# Original First Thunk table
original_first_thunk_table = ''
original_first_thunk_base = offset
original_first_thunk_offset = {}
for (dll_name, functions) in self.functions.items(): # for each dll
original_first_thunk_offset[dll_name] = offset
for function_name in functions: # for each function in this dll
tmp = struct.pack('<I', names_table_offset[function_name])
original_first_thunk_table += tmp
offset += len(tmp)
original_first_thunk_table += '\x00\x00\x00\x00'
offset += 4
# First Thunk Table
first_thunk_table = original_first_thunk_table
first_thunk_base = offset
first_thunk_offset = {}
for (dll_name, offset_in_oft) in original_first_thunk_offset.items():
first_thunk_offset[dll_name] = offset_in_oft - original_first_thunk_base + first_thunk_base
# IAT Table
iat_table = ''
for (dll_name, functions) in self.functions.items(): # for each dll
function = PE.structDictionary(PE.IMAGE_IMPORT_DESCRIPTOR)
function['OriginalFirstThunk'] = original_first_thunk_offset[dll_name]
function['TimeDateStamp'] = 0
function['ForwarderChain'] = 0
function['Name'] = names_table_offset[dll_name]
function['FirstThunk'] = first_thunk_offset[dll_name]
iat_table += PE.packDictionary(PE.IMAGE_IMPORT_DESCRIPTOR, function)
function['OriginalFirstThunk'] = 0
function['TimeDateStamp'] = 0
function['ForwarderChain'] = 0
function['Name'] = 0
function['FirstThunk'] = 0
iat_table += PE.packDictionary(PE.IMAGE_IMPORT_DESCRIPTOR, function)
iat_table += names_table
iat_table += original_first_thunk_table
iat_table += first_thunk_table
return iat_table
from pe import PE
# list export functions
pe = PE('res/obs-aipai.dll')
print("ImageBaseAddr: 0x{0:08x}".format(pe.imagebase))
for api, addr in pe.exports.items():
print("{0} @ 0x{1:08x}".format(api, addr))
print('\n\n')
# list import functions
pe = PE('res/notepad.exe')
print("ImageBaseAddr: 0x{0:08x}".format(pe.imagebase))
for dllname in pe.imports:
for api, addr in pe.imports[dllname].items():
print("{0} ({1}) @ 0x{2:08x} (IAT)".format(api, dllname, addr))
