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aie4ml

PyPI

AMD AIE backend plugin for hls4ml

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README

Version 0.2.1 documentation

aie4ml

aie4ml is an end-to-end compiler that generates optimized AIE firmware automatically, which can be then built and simulated directly using AMD Vitis. It targets the AMD AI Engine (AIE) from model-level frontends and lowers supported operators into AIE graphs and kernels as a standalone AIE project.

Current hardware targets: AIE1, AIE-ML and AIE-MLv2 devices.
Current frontend paths: ONNX for explicit operator graphs, and an optional hls4ml frontend path.

Supported Operators

The full constraints are in docs/support.md.

Operator	AIE1	AIE-ML	AIE-MLv2	Precision (inputs × weights)	Notes
Dense (Gemm, MatMul with constant weights)	✅	✅	✅	int8 × int8, int16 × int8, float32 on all; int16 × int16 and bfloat16 on AIE-ML and AIE-MLv2; FP8 (E4M3) on AIE-MLv2	Optional bias, fused ReLU.
MatMul (two activations)	✅	✅	✅	As Dense	The right operand is 2-D; it may be broadcast over the left operand's batch axes.
Conv2D (including grouped, depthwise and pointwise)	✅	✅	✅	int8 × int8 on all; int16 × int8 on AIE-ML and AIE-MLv2 (int8 or int16 output)	Batch 1, kernels up to 7×7, padding smaller than the kernel, any stride (int16 inputs: horizontal stride 1), no dilation. Fuses bias, ReLU and a following MaxPool. Depthwise runs a channelwise kernel on AIE-ML and AIE-MLv2.
MaxPool	✅	✅	✅	As its Conv2D	2×2, stride 2, directly after a Conv2D (ReLU on either side).
BatchNormalization	✅	✅	✅	—	Folded into the preceding Conv2D or Dense (`QConv2DBatchnorm`, Dense + BatchNormalization, or a folded ONNX export).
ReLU	✅	✅	✅	—	Fused into the Dense or Conv2D.
Add	✅	✅	✅	Both inputs and the output of one type	Same shapes; no broadcasting.
LayerNorm	✅	✅	✅	int8	Last axis.
Softmax	✅	✅	✅	int8 in, uint8 or int16 out	Last axis. Exact integer exponential (beta), or a faster surrogate for models trained with it.
Flatten / Reshape	✅	✅	✅	—	One sample to `[1, K]`, from a Conv2D into a Dense; no data is copied.
Transpose	✖️	✅	✅	—	Of the last two axes.
Slice, Split, Concat	✅	✅	✅	—	Along the boundaries of the producing layer's tiles; no data is copied.

8-bit inputs against 16-bit weights (int8 × int16) are not supported.

Prerequisites

AMD Vitis 2026.1.1 and a valid AIE tools license. (aie4ml tracks the newest AIE compiler; older releases may fail to compile some kernels.)
Python 3.10+.

Installation

pip install "aie4ml[onnx]"     # ONNX frontend
pip install "aie4ml[hls4ml]"   # hls4ml frontend

Keras 3 and QKeras v3 models need hls4ml 1.4. Until it is released, install hls4ml from upstream commit:

pip install qkeras-v3 "tensorflow~=2.16.0" \
    "hls4ml @ git+https://github.com/fastmachinelearning/hls4ml.git@a2abb4d22e7762a870cd46ccf3e07ff6a5622ed5"

Documentation & Tutorials

Documentation and usage: https://github.com/dimdano/aie4ml

Tutorial 1: tutorials/tutorial_1.ipynb Tutorial 2: tutorials/tutorial_2.ipynb

General hls4ml concepts: https://fastmachinelearning.org/hls4ml

Frontends

Frontend	Models
ONNX (preferred)	Quantized operator graphs with QuantizeLinear/DequantizeLinear boundaries (QDQ).
hls4ml	Keras 3 and QKeras v3: Dense, Conv2D, DepthwiseConv2D, QConv2DBatchnorm, MaxPooling2D, Flatten, ReLU activations and LayerNormalization. Split a SeparableConv2D into its depthwise and pointwise layers.

Maintainer

aie4ml is developed and maintained by Dimitrios Danopoulos.

Citation

If aie4ml contributes to your research, please cite the corresponding publications:

@INPROCEEDINGS{11552717,
  author={Danopoulos, Dimitrios and Lupi, Enrico and Sun, Chang and Dittmeier, Sebastian and Kagan, Michael and Loncar, Vladimir and Pierini, Maurizio},
  booktitle={2026 IEEE 34th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)},
  title={AIE4ML: An End-to-End Framework for Compiling Neural Networks for the Next Generation of AMD AI Engines},
  year={2026},
  volume={},
  number={},
  pages={176-184},
  keywords={Tiles;Modeling;Arrays;Kernel;Memory;Information rates;Throughput;System-on-chip;Loading;Engines;ai engines;hls4ml;aie4ml;versal;acceleration;inference},
  doi={10.1109/FCCM68464.2026.00035}}

@misc{danopoulos2026tamingexponentialfastsoftmax,
      title={Taming the Exponential: A Fast Softmax Surrogate for Integer-Native Edge Inference},
      author={Dimitrios Danopoulos and Enrico Lupi and Michael Kagan and Maurizio Pierini},
      year={2026},
      eprint={2604.02292},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2604.02292},
}

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