2025b_fasttrack_source/imc/exprs/date2025/3_adaptive/run_expr_3.py

import tempfile
import time
import pickle
import pandas as pd

from imc_utils.pps_e36311a import PPS_E36311A
from imc_utils.smu_b2902a import SMU_B2902A
from imc_utils.build_config.cortex_m33 import BuildConfigM33
from imc_utils.build_config.test_env import TestEnv
from imc_utils.serial_watch import SerialWatcher

WORKSPACE_ROOT = "/home/ybkim/workspace/imc/imc_freertos_app_m33"
NVM_RESET_BIN = f"{WORKSPACE_ROOT}/imc/utils/nvm_reset.elf"
OPENOCD_SCRIPT = f"{WORKSPACE_ROOT}/imc_freertos_app_m33.cfg"

use_smu = False

def get_build_config(benchmark, config_name):
    config = get_default_build_config()
    config.bench_name = benchmark
    bench_repeat_count = config.bench_repeat_count_small[benchmark]
    config.bench_repeat_count = bench_repeat_count

    if config_name == "pass_count":
        config.use_checkpoint_pass_counter = True
        # config.checkpoint_pass_count = config.pass_count_10ms[benchmark]
        # config.checkpoint_pass_count = config.pass_count_400ms[benchmark]
        # config.checkpoint_pass_count = config.pass_count_167ms[benchmark]
        config.checkpoint_pass_count = config.pass_count_154ms[benchmark]
    
    if config_name == "adaptive":
        config.use_checkpoint_voltage_check = True
        config.split_loop = True
        config.enable_adaptive_loop_pass_count = True
        config.max_loop_ids = 15
        config.loop_opt_debug = False
    
    if config_name == "unroll":
        config.use_checkpoint_voltage_check = True
        config.custom_unroll = True

    return config

def main():
    pps = PPS_E36311A()
    if use_smu:
        volt = 3.3
        smu = SMU_B2902A(voltage=3.3)
    config = get_default_build_config()

    benchmarks = [
        "vBasicMath",
        "vCrc",
        "vFFT",
        "vSha",
        "vStringSearch",
        "vMatMul",
        "vConv2d",
        "vAes",
    ]
    benchmarks = [
        # "vCrc",
        # "vSha",
        "vBasicMath",
        # "vMatMul"
    ]

    configs = ["pass_count", "adaptive", "unroll"]
    # configs = ["adaptive"]

    total_iterations = 20
    target_current_limit = 0.015

    for benchmark in benchmarks:
        for config_name in  configs:
            all_records = []
            config = get_build_config(benchmark, config_name)

            if use_smu:
                smu.set_current_limit(0.1)
                smu.power_on()
            else:
                pps.set_voltage(3.3, 1)
                pps.set_current(0.1, 1)
                pps.output_on(1)

            env = TestEnv(WORKSPACE_ROOT, NVM_RESET_BIN, OPENOCD_SCRIPT)

            with tempfile.TemporaryDirectory() as build_dir:
                binary = env.build_binary(config, build_dir)
                env.clear_nvm_and_load_binary(binary, resume=False)

            if use_smu:
                smu.set_current_limit(target_current_limit)
            else:
                pps.set_current(target_current_limit, 1)

            time.sleep(1)

            env.resume_board(terminate=True)
            watcher = SerialWatcher(benchmark, total_iterations)
            records = watcher.run()

            for record in records:
                record.update(
                    {
                        # "loop_pass_count": loop_pass_count,
                    }
                )
                all_records.append(record)

            df = pd.DataFrame(all_records)
            columns = ["bench_name", "time_taken", "recovery", "stats", "is_correct"]
            print(df[columns])
            save_records(benchmark, config_name, df)

    if use_smu:
        smu.power_off()
    else:
        pps.output_off(1)


def get_default_build_config():
    config = BuildConfigM33()
    config.insert_compiler_checkpoints = True
    config.enable_extension = True
    config.use_checkpoint_pass_counter = False
    config.use_checkpoint_voltage_check = False
    config.bench_infinite_loop = True
    config.print_recovery_message = True
    config.split_loop = False
    config.enable_static_loop_pass_count = False
    config.enable_adaptive_loop_pass_count = False
    config.print_stats = True
    config.custom_unroll = False
    config.loop_opt_debug = False
    return config


def save_records(bench_name, config_name, df):
    with open(f"output/{config_name}/{bench_name}.pickle", "wb") as f:
        pickle.dump(df, f)


if __name__ == "__main__":
    main()