Alkaid: A Library for Kernel Accelerator Implemented on Device

Tests Documentation Cov

alkaid is a lightweight compiler for generating low-latency static-dataflow kernels for FPGAs. It traces quantized arithmetic into ALIR, applies distributed-arithmetic optimization through CMVM where useful, and emits RTL or HLS projects.

The supported code generation targets are:

  • RTL: Verilog and VHDL 2008, with optional pipelining.
  • HLS: Vitis HLS, HLSlib, and oneAPI-style C++.
  • XLS: experimental Verilog generation and JIT execution through xls-python.

Models can be described with the NumPy-like symbolic tracing API, loaded from serialized ALIR JSON/JSON.GZ, or converted through plugins. The package includes top-level keras, torch, and alkaid example tracer plugins; Keras and Torch support can also be extended with second-level alkaid_keras and alkaid_torch entry points.

Installation

pip install alkaid

Binary wheels are published for Linux x86_64 and macOS ARM64. Building from source requires Python 3.10 or newer, NumPy, meson-python, and a C++20 compiler with OpenMP support.

pip install --no-build-isolation -e '.[tests]'

Use pip install 'alkaid[docs]' for documentation dependencies. Install xls-python when using the XLS backend outside the test extra.

CLI

alkaid convert model.keras /tmp/rtl_output --flavor verilog --latency-cutoff 5
alkaid convert model.json.gz /tmp/hls_output --flavor vitis
alkaid report /tmp/rtl_output --sort-by comb_metric

See the documentation for tracing, plugin, ALIR, and backend details.

License

LGPLv3. See the LICENSE file for details.

Project links

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Credits

Author: Chang Sun

GitHub Statistics

Data verified by PyPI on Aug 14, 2026
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License expression

LGPL-3.0-or-later
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Requires

Python >=3.10

Provides Extra

docs tests

Tags

CMVM distributed arithmetic high-level synthesis HLS Complier machine learning RTL Generator

Classifiers

Report project as malware

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