diff --git a/llvm/projects/soc_simulator/src/driver_new_config.py b/llvm/projects/soc_simulator/src/driver_new_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..115237dac51c96b47d02c84a603d98bdcf0b84a4
--- /dev/null
+++ b/llvm/projects/soc_simulator/src/driver_new_config.py
@@ -0,0 +1,328 @@
+from collections import defaultdict
+import os
+import subprocess
+import sys
+
+class Driver:
+ fp16_swing = 8
+
+ class ApproxTypes:
+ FP16 = 0
+ FP32 = 1
+ PROMISE = 2
+ PERF = 3
+
+ results_time_key = "Time"
+ results_energy_key = "Energy"
+
+
+ def driver(self):
+ self.__parse_tensor_layer_file()
+ self.__parse_tensor_table()
+ self.__run_simulations()
+ self.__display_results()
+
+
+ def __init__(self, layer_filename, table_filename, config_filename, results_filename):
+ self.__layer_filename = layer_filename
+ self.__table_filename = table_filename
+ self.__config_filename = config_filename
+ self.__results_filename = results_filename
+
+ # NOTE: Use an OrderedDict if we want to search by operation name
+ # Using a list bc we care about the order the data is read in
+ # since it corresponds to the data in the configuration file
+ self.__tensor_layers = []
+
+ # [layer_name][operation_name][cols]
+ # Operation names need to be stored in order of insertion
+ self.__tensor_table = defaultdict(lambda: list(defaultdict(str)))
+
+ # [Time/Energy][number corresponding to order the layer config was read in] = time/energy
+ self.__aggregate_results = defaultdict(lambda: defaultdict(float))
+ self.__config_count = 0
+
+
+ @staticmethod
+ def is_conv(operation_name):
+ return operation_name.startswith("Conv")
+
+
+ @staticmethod
+ def is_nml(operation_name):
+ return operation_name.startswith("NML")
+
+
+ @staticmethod
+ def is_fc(operation_name):
+ return operation_name.startswith("FC")
+
+
+ def __parse_tensor_layer_file(self):
+ if not os.path.isfile(self.__layer_filename):
+ print("ERROR: %s was not found." % self.__layer_filename)
+ exit(1)
+
+ layer_file = open(self.__layer_filename, "r")
+ for line in layer_file:
+ layer_data = line.strip().split(',')
+ layer_name = layer_data[0]
+
+ tensor_layer = defaultdict(str)
+ tensor_layer["Name"] = layer_name
+
+ if Driver.is_conv(layer_name):
+ tensor_layer["N"] = float(layer_data[1])
+ tensor_layer["Cin"] = float(layer_data[2])
+ tensor_layer["H"] = float(layer_data[3])
+ tensor_layer["W"] = float(layer_data[4])
+ tensor_layer["Cout"] = float(layer_data[5])
+ tensor_layer["Kh"] = float(layer_data[7])
+ tensor_layer["Kw"] = float(layer_data[8])
+ tensor_layer["Sh"] = float(layer_data[9])
+ tensor_layer["Sw"] = float(layer_data[10])
+
+ elif Driver.is_fc(layer_name):
+ tensor_layer["RA"] = float(layer_data[1])
+ tensor_layer["CA"] = float(layer_data[2])
+ tensor_layer["RB"] = float(layer_data[3])
+ tensor_layer["CB"] = float(layer_data[4])
+
+ elif not Driver.is_nml(layer_name): # TODO should we store data for NMLs?
+ print("ERROR: Invalid layer name %s" % layer_name)
+ exit(1)
+
+ self.__tensor_layers.append(tensor_layer)
+ layer_file.close()
+
+
+ def __parse_tensor_table(self):
+ if not os.path.isfile(self.__table_filename):
+ print("ERROR: %s was not found." % self.__table_filename)
+ exit(1)
+ table_file = open(self.__table_filename, "r")
+ line = table_file.readline().strip()
+
+ while line:
+ # Line here MUST be a header or there's a bug
+ # Get the description of the layer
+ assert(line.startswith("**"))
+
+ header_contents = line.split(' ')[1:]
+ layer_name = header_contents[0]
+ num_ops = int(header_contents[1])
+ col_names = header_contents[2:]
+
+ layer_operations = []
+
+ # Go through all operations in the layer
+ for op_count in range(num_ops):
+ operation_data = defaultdict(str)
+
+ line = table_file.readline().strip()
+ op_data = line.split(' ')
+ op_name = op_data[0]
+ operation_data["Name"] = op_name
+
+ # Number of data items (#s) needs to match up with the # of cols
+ assert(len(op_data) - 1 == len(col_names))
+
+ # Go through all data items (each col element) per operation
+ for i in range(len(col_names)):
+ operation_data[col_names[i]] = float(op_data[i + 1])
+
+ layer_operations.append(operation_data)
+
+ self.__tensor_table[layer_name] = layer_operations
+ line = table_file.readline().strip()
+ table_file.close()
+
+
+ @staticmethod
+ def is_promise(config_layer):
+ return float(config_layer.split(' ')[0]) < Driver.fp16_swing
+
+
+ def __quantize(self, curr_layer, prev_layer, h2f_f2h_operation_ind, layer_data):
+ if curr_layer == prev_layer or curr_layer == Driver.ApproxTypes.PROMISE \
+ or prev_layer == Driver.ApproxTypes.PROMISE: # No quantization needed
+ return 0.0, 0.0
+
+ layer_name = layer_data["Name"]
+
+ # NOTE: Ignoring logic where curr == promise or prev == promise bc
+ # smartDMA is always true so we'd return near the beginning of the method
+
+ # Get h2f/f2h data using the first tensor operation in the layer
+ # (which is why order matters in the tensor table)
+ print(layer_name, self.__tensor_table[layer_name])
+ tensor_op_row = self.__tensor_table[layer_name][h2f_f2h_operation_ind]
+ if curr_layer == Driver.ApproxTypes.FP32:
+ time = tensor_op_row["h2f_time"]
+ energy = tensor_op_row["h2f_energy"]
+ elif curr_layer == Driver.ApproxTypes.FP16:
+ time = tensor_op_row["f2h_time"]
+ energy = tensor_op_row["f2h_energy"]
+
+ print("Quantization: (%f, %f)" % (time, energy))
+ return (time, energy)
+
+
+ def __run_promise_simulation(self, swing, layer_data):
+ layer_name = layer_data["Name"]
+ patch_factor = 1
+
+ if Driver.is_conv(layer_name):
+ rows_a = layer_data["N"] * layer_data["H"] * layer_data["W"] \
+ / (layer_data["Sh"] * layer_data["Sw"])
+ cols_a = layer_data["Cin"] * layer_data["Kh"] * layer_data["Kw"]
+ rows_b = cols_a
+ cols_b = layer_data["Cout"]
+ patch_factor = layer_data["Kh"] * layer_data["Kw"]
+ elif Driver.is_fc(layer_name):
+ rows_a = layer_data["RA"]
+ cols_a = layer_data["CA"]
+ rows_b = cols_a
+ cols_b = layer_data["CB"]
+ else:
+ print("PROMISE can't run whatever this layer is.")
+ exit(1)
+ # Run promise simulator
+ # TODO need to print time and energy in the ptm runner so we can pipe it
+ output = subprocess.Popen(["./ptm", str(rows_a), str(cols_a), str(rows_b), \
+ str(cols_b), str(patch_factor), str(swing)], \
+ stdout = subprocess.PIPE, stderr = subprocess.PIPE).communicate()[0]
+ total_time_energy = output.strip().split(',')
+
+ assert(len(total_time_energy) == 2)
+ print("PROMISE: (%s, %s)" % (total_time_energy[0], total_time_energy[1]))
+ return float(total_time_energy[0]), float(total_time_energy[1])
+
+
+ def __run_simulations(self):
+ if not os.path.isfile(self.__config_filename):
+ print("ERROR: %s was not found" % self.__config_filename)
+ exit(1)
+
+ config_file = open(self.__config_filename, "r")
+
+ line = config_file.readline().strip()
+
+ while line:
+ assert(line.startswith("+++++"))
+ config_name = config_file.readline().strip().split(' ')[0] # Next line = configuration name
+ print("CONFIGURATION")
+
+ line = config_file.readline().strip()
+ layer_ind = 0 # NOTE can also use the leftmost number in the currl ine
+
+ prev_layer = Driver.ApproxTypes.FP32
+ curr_layer = None
+
+ while not line.startswith("-----"):
+ layer_info = line.split(' ')
+ layer_data = self.__tensor_layers[layer_ind]
+ layer_name = layer_data["Name"]
+
+ if layer_info[1] == "promise":
+ print("Running layer %s on PROMISE" % layer_name)
+ curr_layer = Driver.ApproxTypes.PROMISE
+
+ swing = int(layer_info[3])
+ time, energy = self.__run_promise_simulation(swing, layer_data)
+ print(time, energy)
+ self.__aggregate_results[Driver.results_time_key][self.__config_count] += time
+ self.__aggregate_results[Driver.results_energy_key][self.__config_count] += energy
+
+ elif layer_info[1] == "gpu":
+ # Parse each individual tensor operation
+ # TODO not portable bc there can be multiple numbers after each approx later on
+ total_time = 0
+ total_energy = 0
+
+ tensor_ind = 0
+ for i in range(2, len(layer_info), 3):
+ tensor_op = layer_info[i]
+ approx_type = layer_info[i + 1]
+ approx_num = layer_info[i + 2] # only matters if perf
+
+ if approx_type == "fp16":
+ curr_layer = Driver.ApproxTypes.FP16
+ elif approx_type == "fp32":
+ curr_layer = Driver.ApproxTypes.FP32
+ elif approx_type == "perf":
+ curr_layer = DriverApproxTypes.PERF
+ else:
+ assert(False)
+
+ quant_time, quant_energy = self.__quantize(curr_layer, prev_layer, tensor_ind, layer_data)
+ time, energy = self.__run_gpu_simulation(curr_layer, layer_name, tensor_ind, approx_num)
+ total_time += time
+ total_energy += energy
+
+ tensor_ind += 1
+
+ self.__aggregate_results[Driver.results_time_key][self.__config_count] += total_time
+ self.__aggregate_results[Driver.results_energy_key][self.__config_count] += total_energy
+
+ layer_ind += 1
+ line = config_file.readline().strip()
+
+ self.__config_count += 1
+ line = config_file.readline().strip()
+
+ config_file.close()
+
+
+ def __run_gpu_simulation(self, curr_layer, layer_name, tensor_ind, approx_num):
+ tensor_info = self.__tensor_table[layer_name][tensor_ind]
+
+ if curr_layer == Driver.ApproxTypes.FP32:
+ time = tensor_info["fp32_time"]
+ energy = tensor_info["fp32_energy"]
+
+ elif curr_layer == Driver.ApproxTypes.FP16:
+ time = tensor_info["fp16_time"]
+ energy = tensor_info["fp16_energy"]
+
+ elif curr_layer == Driver.ApproxTypes.PERF:
+ time = tensor_info["perf%s_energy" % approx_num]
+ energy = tensor_info["perf%s_energy" % approx_num]
+
+ print("GPU: (%f, %f)" % (time, energy))
+ return time, energy
+
+
+ def __display_results(self):
+ results_file = open(self.__results_filename, "w")
+ attributes_to_print = [Driver.results_time_key, Driver.results_energy_key]
+
+ for attribute in attributes_to_print:
+ results_file.write("%s\n" % attribute)
+ results_file.write("Configuration,Total,Improvement\n")
+
+ baseline_val = self.__aggregate_results[attribute][0]
+ print(baseline_val)
+ best_config = None
+ best_result = None
+
+ for config_ind in range(self.__config_count):
+ results_file.write("c%d" % config_ind)
+ time_or_energy_val = self.__aggregate_results[attribute][config_ind]
+
+ # Using repr to keep all decimal digits when writing to file
+ results_file.write(",%s" % repr(time_or_energy_val))
+ results_file.write(",%s\n" % repr(baseline_val / (time_or_energy_val + 0.0001)))
+
+ if not best_result or time_or_energy_val < best_result:
+ best_result = time_or_energy_val
+ best_config = config_ind
+ results_file.write("\nc%d,%s\n\n" % (best_config, repr(self.__aggregate_results[attribute][best_config])))
+ results_file.close()
+
+
+if __name__ == "__main__":
+ if len(sys.argv) != 5:
+ print("Usage: python driver.py <layer info> <tensor info> <configurations> <results file>")
+ exit(1)
+ Driver(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]).driver()