sparsepixels 0.2.1

Efficient convolution for sparse data on FPGAs

SparsePixels: Efficient convolution for sparse data on FPGAs

Note: We are actively working on hls4ml integration to auto-convert sparse models to HLS, along with a major upgrade with partial parallelization and streaming for sparse layers in HLS. Stay tuned!

Installation

With Python >= 3.10:

pip install sparsepixels

Getting Started

Import sparse layers and quantization library (HGQ2):

import keras
from keras.layers import Flatten, Activation, ReLU
from hgq.layers import QConv2D, QDense
from hgq.config import QuantizerConfigScope, LayerConfigScope
from sparsepixels.layers import InputReduce, QConv2DSparse, AveragePooling2DSparse

Build an example sparse CNN within HGQ2 quantization scopes:

with (
    QuantizerConfigScope(place='all', default_q_type='kbi', overflow_mode='SAT_SYM'),
    QuantizerConfigScope(place='datalane', default_q_type='kif', overflow_mode='WRAP'),
    LayerConfigScope(enable_ebops=False, enable_iq=False),
):
    x_in = keras.Input(shape=(x_train.shape[1], x_train.shape[2], x_train.shape[3]), name='x_in')

    # Sparse input reduction: retain up to n_max_pixels active pixels
    x, keep_mask = InputReduce(n_max_pixels=20, threshold=0.1, name='input_reduce')(x_in)

    # Sparse convolution
    x = QConv2DSparse(filters=3, kernel_size=3, name='conv1', padding='same', strides=1)([x, keep_mask])
    x = ReLU(name='relu1')(x)

    # Sparse pooling
    x, keep_mask = AveragePooling2DSparse(2, name='pool1')([x, keep_mask])

    x = Flatten(name='flatten')(x)
    x = QDense(10, name='dense1', activation='relu')(x)
    x = Activation('softmax', name='softmax')(x)

model = keras.Model(x_in, x)

We are working on hls4ml integration that auto parses the sparse layers into HLS.

Documentation

Citation

If you find this useful in your research, please consider citing:

@article{Tsoi:2025nvg,
    author = "Tsoi, Ho Fung and Rankin, Dylan and Loncar, Vladimir and Harris, Philip",
    title = "{SparsePixels: Efficient Convolution for Sparse Data on FPGAs}",
    eprint = "2512.06208",
    archivePrefix = "arXiv",
    primaryClass = "cs.AR",
    month = "12",
    year = "2025"
}