The correct unit of optimization in heterogeneous computing is not the operation — it is the crossing.

CrossingBench is a reproducible microbenchmark for validating the Domain Crossing Law: in heterogeneous compute systems, energy efficiency is dominated by crossing volume weighted by boundary cost in crossing-dominated regimes (ε → 1).

The formal law statement, definitions, and empirical basis are in docs/CROSSING_LAW.md. All energy parameters and their sources are in docs/HYPOTHESES.md.

📄 Working Paper (Feb 2026)
CrossingFlow: Minimizing Domain Crossings in Heterogeneous Systems Under Error Constraints
Download PDF

Heterogeneous systems — analog CIM, chiplets, near-memory, multi-voltage — achieve impressive intra-domain efficiency. Yet system-level gains consistently fall short because domain crossings (ADC/DAC, DRAM fetch, inter-die transfer, level shifter) cost 5× to 32,000× more per byte than the compute they enable.

Figure 1. Crossing energy fraction vs. crossing volume for three boundary types. The analog curve shifts left due to ultra-low intra-domain compute energy (0.1 pJ/B vs 0.25 pJ/B digital baseline). Shaded regions: ±20% uncertainty on β. Reproduce with python examples/plot_dominance.py.

CrossingBench lets you:

• Measure crossing dominance via the elasticity metric ε
• Sweep crossing volume and observe the compute→crossing transition
• Compare baseline vs crossing-reduced schedules
• Validate the law on your own boundary parameters

C_total = C_intra + Σ_b (α_b · events_b + β_b · bytes_b)

Default parameters match published measurements at 7nm (see docs/HYPOTHESES.md). Override any parameter on the command line.

python -m pip install -e .

Or run directly without installing:

python -m crossingbench --help

Requires Python ≥ 3.9. Zero runtime dependencies (pure Python).

crossingbench sweep \
  --boundary analog --compute analog \
  --bytes 262144 --cross_min 256 --cross_max 524288 \
  --steps 12 --out analog.csv

Output: CSV with crossing_fraction and epsilon_local at each point.

crossingbench compare \
  --boundary analog --compute analog \
  --bytes 262144 --cross_bytes 196608 --reduce_factor 3

Output: energy gain, crossing fractions, and effective elasticity.

bash examples/reproduce_three_boundaries.sh

python examples/real_workloads.py

python examples/plot_dominance.py

Override: --beta 2.5 --alpha 50 --compute_pj_per_byte 0.1

CrossingBench/
├── docs/
│   ├── figures/
│   │   └── crossing_dominance.png  # Three-boundary dominance figure
│   ├── CROSSING_LAW.md             # The law (definitions, decomposition, results)
│   └── HYPOTHESES.md               # Every pJ/byte value traced to its source
├── paper/
│   └── CrossingFlow_Domain_Crossing_Law_v1_2026.pdf
├── examples/
│   ├── plot_dominance.py            # Regenerate the dominance figure
│   ├── reproduce_three_boundaries.sh
│   └── real_workloads.py            # ResNet-50 + GPT-2 validation
├── src/crossingbench/
│   ├── core.py                      # Cost model, sweep, compare
│   ├── cli.py                       # Command-line interface
│   └── io.py                        # CSV output
├── tests/
│   └── test_core.py                 # Unit tests
├── data/                            # Reference CSVs
└── pyproject.toml                   # Package metadata (hatchling)

python -m pip install -e '.[dev]'
pytest
ruff check .

If you use CrossingBench in academic work, please cite:

Morissette, J. (2026). CrossingBench v0.1.0: Microbenchmark for Boundary-Dominated Energy Scaling. Zenodo. https://doi.org/10.5281/zenodo.18653510

BibTeX:

@software{morissette2026crossingbench,
  author    = {Morissette, Jessy},
  title     = {CrossingBench v0.1.0: Microbenchmark for Boundary-Dominated Energy Scaling},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.18653510},
  url       = {https://doi.org/10.5281/zenodo.18653510}
}

Apache-2.0 (see LICENSE).