Metadata-Version: 2.4
Name: ravel-hls
Version: 1.5.1
Summary: Rate-Aware Vectorized Engine for Low-latency FPGA inference
License-Expression: Apache-2.0
Project-URL: Repository, https://github.com/albertc9/RAVEL
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: hls4ml==1.2.0
Requires-Dist: hgq2==0.1.7
Requires-Dist: Jinja2==3.1.6
Requires-Dist: keras==3.12.1
Requires-Dist: numpy==1.26.4
Requires-Dist: quantizers==1.2.2
Requires-Dist: PyYAML==6.0.3
Requires-Dist: tensorflow-cpu==2.20.0; platform_system == "Linux"
Provides-Extra: test
Requires-Dist: pytest<10,>=8; extra == "test"
Dynamic: license-file

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# RAVEL

RAVEL (Rate-Aware Vectorized Engine for Low-latency) generates a specialized,
hls4ml-compatible FPGA inference project. Aria 1.5.1 analyzes qualified
Keras/HGQ2 models through hls4ml's `ModelGraph`, extracts learned fixed-point
types and parameters, and resolves P2/P4 temporal packing with Dense x1/x2.
New conversions default to P4/D2 with sequential packed Dense weights.
The model family is capable of processing an 8-channel ADC stream with a rate of up to 4.4 GSa/s on the KU5P.
This model distinguishes in real time between neutrino signals generated by Askaryn Radiation and noise, and can detect over 99% of neutrinos at a trigger rate of 1 Hz.

## Performance

### Like-for-like comparison

Same model, hls4ml configuration, KU5P target, and 5 ns clock constraint. OOC
resources and WNS are single-core Vivado 2023.2 post-route results. Resource
counts are followed by KU5P utilization. II means Initiation Interval.

| Flow | II | Latency (cycles) | HLS clock (ns) | OOC BRAM tile | OOC DSP | OOC registers | OOC LUTs | OOC WNS (ns) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| Vanilla hls4ml | 3076 | 3084 | 3.619 | 5.5 (1.15%) | 1 (0.05%) | 21745 (5.01%) | 18312 (8.44%) | +0.206 |
| RAVEL Aria 1.1.0 P2/D1 | 178 | 183 | 3.647 | 1.0 (0.21%) | 7 (0.38%) | 2953 (0.68%) | 5332 (2.46%) | +1.121 |
| RAVEL Aria 1.3.0 P4/D2 | 94 | 99 | 3.502 | 0.5 (0.10%) | 14 (0.77%) | 3709 (0.85%) | 7861 (3.62%) | +0.817 |
| RAVEL Aria 1.4.0 P4/D2 | 94 | 99 | 3.402 | 1.5 (0.31%) | 15 (0.82%) | 3622 (0.83%) | 6981 (3.22%) | +0.524 |

At 200 MHz, 1 GSa/s equals 3.90625 million 256-sample chunks/s and requires
60.08/3.48/1.84/1.84 equivalent cores for vanilla/Aria 1.1/1.3/1.4. Relative
to vanilla, Aria 1.1/1.3/1.4 reduce equivalent core count by
94.2%/96.9%/96.9%, BRAM by 98.9%/99.7%/99.2%, DSP by 59.5%/57.2%/54.2%,
registers by 99.2%/99.5%/99.5%, and LUTs by 98.3%/98.7%/98.8%. These
linearized estimates allow fractional core counts and exclude all other FPGA
logic.

The throughput requirements of ARIANNA, RNO-G, and IceCube-Gen2 are already met by the current system design ([AI Trigger System, v3.3.0](https://github.com/NuDAQ/AI-Trigger-System/tree/v3.3.0)). For models with similar architectures and size, processing speed and power consumption are no longer limiting factors.

More Information about the reference implementation, please see the [performance](https://github.com/NuDAQ/CNN-Core-Generator/tree/d37a0a6dc31cf5a38f0d241f47e21263e0fe9e67#performance) of the CNN-Core-Generator.

## Install

Use a clean Python 3.11 virtual environment on Linux:

```bash
python -m pip install ravel-hls
```

## Python API

```python
import ravel_hls as ravel

config = {
    "HLS": {
        "Backend": "Vitis",
        "IOType": "io_stream",
        "Part": "xcku5p-ffvb676-2-e",
        "ClockPeriod": 5.0,
    },
    "Verification": {"Mode": "required", "Samples": 32, "Seed": 19},
    "Vitis": {"Run": False},
}

analysis = ravel.analyze("model.keras", config)
project = ravel.convert("model.keras", "cnn_core", config)
print(project.status)
```

`Optimization` is optional. Omission selects the versioned aggressive default:

```python
config["Optimization"] = {
    "TemporalPacking": 2,  # 2 or 4
    "DenseParallelism": 1,  # 1 or 2
}
```

Each axis may be set independently; an omitted axis keeps its aggressive
default. `ravel.refresh(project, model_or_parameters)` reuses the recorded
architecture contract and rejects changes that require an ordinary conversion.

`Vitis.Run` defaults to `False`. Set it to `True` to run
`vitis_hls -f build_prj.tcl` after atomic project publication and automatically
record the synthesis report. The default Vitis stages are reset and synthesis;
CSim, CoSim, validation, export, and Vivado synthesis remain disabled unless
their booleans under `Vitis.Stages` are enabled explicitly. The same operation
can be requested later with `project.build()`.

The concise project lifecycle is `analyze`, `convert`, `refresh`,
`Project.open(path)`,
`project.build()`, `project.record(report_dir)`, and `project.link()`. The CLI
command `ravel-hls inspect PROJECT --json` performs full source-integrity
checking; add `--fast` when payload hashing should be skipped.

## Parameter packages

`Parameters` stores portable generation-relevant inference state without
generated HLS sources or executable Python objects:

```python
parameters = ravel.Parameters.extract(model)
parameters.save("trained.ravelparams")

project = ravel.Project.open("cnn_core")
project.refresh(ravel.Parameters.load("trained.ravelparams"))
```

The schema-v2 deterministic archive contains JSON plus NPY arrays compiled from
the clean `ModelGraph`. Tensors are addressed by canonical operation and role,
not Keras layer names or variable order. Structure, numeric descriptors, and
layered hashes are checked before complete staged regeneration. Without
embedded known-answer evidence, package refresh can prove baseline-to-optimized
equivalence in `auto` mode but `required` mode rejects it. A package is not
encrypted.

## Other Information

See the executable [CNN-for-Arianna reference](references/cnn_for_arianna/README.md),
[architecture](docs/architecture.md), [compatibility](docs/compatibility.md), and
[project format](docs/project-format.md) for the full contracts. The
[Aria 1.5.1 RTL evidence](references/qualification/aria_1_5_1_full_width/README.md)
records the fixed three-model release subset and its throughput/resource tradeoff.

## Our Project used RAVEL

- [CNN Core Generator](https://github.com/NuDAQ/CNN-Core-Generator)
- [AI Trigger System](https://github.com/NuDAQ/AI-Trigger-System)
- [Hi-Lo Gated CNN Trigger](https://github.com/NuDAQ/Hi-Lo-Gated-CNN-Trigger)
- And more!

## The Future Plan

RAVEL will evolve from the closed, qualified specialization flow into a
general rate-aware FPGA inference generator. Plans for higher versions
are tentative.

Higher versions will focus primarily on expanding functionality and model support. At present, Nocturne 2.0 is expected to bring the target model into its highest practical throughput range. Further versions may still achieve higher throughput, but the remaining headroom is expected to be quite limited.

- **Aria 1.x** Continue improving the closed P2/P4 x D1/D2 specialization set,
  deterministic project lifecycle, verification, and tool compatibility. For this version,
  RAVEL's goal is simply to design an efficient converter for models currently in 
  use or planned for use for 
  high-energy neutrino experiments, e.g., ARIANNA, RNO-G, and IceCube-Gen2.
- **Nocturne 2.x** Generalize model support, add P8 where system bandwidth and
  scheduling permit it, and derive balanced layer-level parallelism.
- **Rhapsody 3.x** Support multiple independent inference contexts
  within one IP, with configurable resource sharing, duplication.
- **Requiem 4.x** Select internal parallelism, IP
  replication, and lane scheduling according to input rate, internal interval, latency, and FPGA resource constraints.

## License
This project licensed under Apache-2.0. See [LICENSE](LICENSE).
