def _retrievePowerData()

in benchmarking/utils/monsoon_power.py [0:0]

• 37 lines of code
• 7 McCabe index (conditional complexity)

def _retrievePowerData(power_data, start_idx, end_idx, num_iters):
    data = {}
    if start_idx < 0 or end_idx < 0:
        return data

    # get base current. It is just an approximation
    THRESHOLD = 150
    num = len(power_data)
    i = end_idx
    sum = 0
    count = 0
    for i in range(end_idx, num):
        if power_data[i]["current"] < THRESHOLD:
            sum += power_data[i]["current"]
            count += 1
    base_current = sum / count if count > 0 else 0

    energy = 0
    prev_time = power_data[start_idx - 1]["time"]
    for i in range(start_idx, end_idx):
        entry = power_data[i]
        curr_time = entry["time"]
        energy += (
            entry["voltage"]
            * (entry["current"] - base_current)
            * (curr_time - prev_time)
        )
        prev_time = curr_time
    total_time = power_data[end_idx]["time"] - power_data[start_idx]["time"]
    power = energy / total_time
    energy_per_inference = energy / num_iters
    latency = total_time * 1000 * 1000 / num_iters
    data["energy"] = _composeStructuredData(energy_per_inference, "energy", "mJ")
    data["power"] = _composeStructuredData(power, "power", "mW")
    data["latency"] = _composeStructuredData(latency, "latency", "uS")
    getLogger().info("Base current: {} mA".format(base_current))
    getLogger().info("Energy per inference: {} mJ".format(energy_per_inference))
    getLogger().info("Power: {} mW".format(power))
    getLogger().info("Latency per inference: {} uS".format(latency))
    return data