ecal-energy 0.1.0

Analytical estimation of the energy cost of the AI lifecycle (J/bit)

eCAL

Analytical estimation of the energy cost of the AI lifecycle (J/bit).

eCAL computes the total energy consumed across the full AI model lifecycle — data transmission, preprocessing, training, evaluation, and inference — using closed-form FLOP formulas and hardware power profiles.

Published in IEEE Journal on Selected Areas in Communications (JSAC), 2026.

Documentation

Installation

From source (recommended for development)

git clone https://github.com/sensorlab/eCAL.git
cd eCAL
pip install -e ".[dev]"

From PyPI

pip install ecal-energy

Dependencies only (legacy)

pip install -r requirements.txt

Quickstart

Python API

import ecal

result = ecal.estimate(
    model_type="MLP",
    model_params={"num_layers": 3, "din": 10, "dout": 2},
    num_samples=1000,
    num_epochs=50,
    hardware="apple_m2",
)

print(f"Total energy: {result['total']:.4f} J")
print(f"eCAL: {result['ecal_j_per_bit']:.2e} J/bit")

CLI

# Estimate energy for an MLP
ecal estimate --model MLP --layers 3 --epochs 50 --hardware apple_m2

# JSON output
ecal estimate --model Transformer --layers 6 --hardware nvidia_h100_sxm --json

# List available hardware profiles
ecal profiles

# Version
ecal --version

Legacy (RunCalculator.py)

# Edit configs/CalculatorConfig.py, then:
python RunCalculator.py

Supported Models

Model	FLOP Calculator	Key Parameters
MLP	MLPCalculator	num_layers, din, dout
CNN	CNNCalculator	num_cnv_layers, num_pool_layers, i_r, k_r
KAN	KANCalculator	num_layers, grid_size, din, dout
Transformer	TransformerCalculator	context_length, embedding_size, num_heads, num_decoder_blocks

Hardware Profiles

Profile	FP32 FLOPS	TDP (W)	Device
apple_m2	3.6 TFLOPS	22	mps
nvidia_a100_80gb	19.5 TFLOPS	400	cuda
nvidia_h100_sxm	67 TFLOPS	700	cuda
generic_cpu	1 TFLOPS	100	cpu
generic_edge	0.01 TFLOPS	15	cpu

Architecture

                    ecal.estimate()
                         |
        +--------+-------+-------+--------+
        |        |       |       |        |
  Transmission  Preproc  Training  Eval  Inference
        |        |       |       |        |
        v        v       v       v        v
   Protocol   FLOP    FLOP     FLOP    FLOP
   Configs   Calcs   Calcs    Calcs   Calcs
                     (per model type)
                         |
                    Hardware Profile
                   (FLOPS, power, TDP)
                         |
                   Energy = time * power
                         |
                  eCAL = total_E / total_bits

Configuration

Protocol configs are in configs/ProtocolConfigs.py — supports 7 OSI layers with multiple protocol options (HTTP, TCP, IPv4, WiFi, Bluetooth, etc.).

Calculator parameters are in configs/CalculatorConfig.py for the legacy RunCalculator.py interface.

Development

pip install -e ".[dev]"
pytest                    # run tests
ruff check src/ tests/    # lint
mypy src/ecal/            # type check

Citation

If you use this tool please cite our paper:

@ARTICLE{11298182,
  author={Chou, Shih-Kai and Hribar, Jernej and Hanžel, Vid and Mohorčič, Mihael and Fortuna, Carolina},
  journal={IEEE Journal on Selected Areas in Communications},
  title={The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks},
  year={2026},
  volume={44},
  number={},
  pages={2427-2443},
  keywords={Artificial intelligence;Measurement;Costs;Energy consumption;Carbon dioxide;Training;Standards;Data centers;Open systems;Energy efficiency;AI model lifecycle;energy consumption;carbon footprint;metric;methodology},
  doi={10.1109/JSAC.2025.3642835}}

Other related work:

@INPROCEEDINGS{11349371,
  author={Chou, Shih-Kai and Hribar, Jernej and Bertalanič, Blaž and Mohorčič, Mihael and Lagkas, Thomas and Sarigiannidis, Panagiotis and Fortuna, Carolina},
  booktitle={2025 IEEE Conference on Network Function Virtualization and Software-Defined Networking (NFV-SDN)},
  title={Energy Cost of the AI/ML Workflow in O-RAN},
  year={2025},
  volume={},
  number={},
  pages={1-6},
  keywords={Training;Measurement;Adaptation models;Costs;Open RAN;Hardware;Energy efficiency;Complexity theory;Artificial intelligence;Optimization;sustainable 6G networks;O-RAN;AI/ML Workflow;eCAL;lifecycle;energy;Carbon Footprint},
  doi={10.1109/NFV-SDN66355.2025.11349371}}

@INPROCEEDINGS{10849732,
  author={Chou, Shih-Kai and Hribar, Jernej and Mohorčič, Mihael and Fortuna, Carolina},
  booktitle={2024 IEEE Conference on Standards for Communications and Networking (CSCN)},
  title={Towards the Standardization of Energy Efficiency Metrics of the AI Lifecycle in 6G and Beyond},
  year={2024},
  volume={},
  number={},
  pages={187-190},
  keywords={Measurement;6G mobile communication;Energy consumption;Costs;Energy measurement;Energy efficiency;Computational efficiency;Quality of experience;Artificial intelligence;Standards;6G;AI-native network;energy efficiency},
  doi={10.1109/CSCN63874.2024.10849732}}

License

BSD 3-Clause License. See LICENSE.