SparsePixels: Efficient Convolution for Sparse Data on FPGAs

• URL: http://arxiv.org/abs/2512.06208v1
• Date: Fri, 05 Dec 2025 23:04:44 GMT
• Title: SparsePixels: Efficient Convolution for Sparse Data on FPGAs
• Authors: Ho Fung Tsoi, Dylan Rankin, Vladimir Loncar, Philip Harris,
• Abstract summary: In some image data, input features can be spatially sparse, and semantic information may occupy only a small fraction of the input pixels.<n>We introduce SparsePixels, a framework for efficient convolution for spatially sparse image data on FPGAs.<n>Our approach implements a special class of CNNs that selectively retain and compute on a small subset of pixels that are active while ignoring the rest.
• Score: 0.10425052724357148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inference of standard CNNs on FPGAs often incurs high latency and a long initiation interval due to the deep nested loops required to densely convolve every input pixel regardless of its feature value, especially when the image size is large. However, in some image data, input features can be spatially sparse, and semantic information may occupy only a small fraction of the input pixels. In this case most computation would be wasted on empty regions. In this work, we introduce SparsePixels, a framework for efficient convolution for spatially sparse image data on FPGAs, targeting fast inference applications in constrained environments with latency requirements of microseconds or below. Our approach implements a special class of CNNs that selectively retain and compute on a small subset of pixels that are active while ignoring the rest. We show that, for example, in a neutrino physics dataset for identifying neutrino interactions in LArTPC images that have around 4k input pixels but are naturally very sparse, a standard CNN with a compact size of 4k parameters incurs an inference latency of 48.665 $μ$s on an FPGA, whereas a sparse CNN of the same base architecture computing on less than 1% of the input pixels results in a $\times 73$ inference speedup to 0.665 $μ$s, with resource utilization well within on-chip budgets, trading only a small percent-level performance loss. At least one-order-of magnitude speedups with comparable performance are also demonstrated in similar datasets with sparse image patterns. This work aims to benefit future algorithm developments for fast and efficient data readout in modern experiments such as the trigger and data acquisition systems at the CERN Large Hadron Collider. For easy adoption, we have developed a library to support building sparse CNNs with quantization-aware training, as well as an HLS implementation for FPGA deployment.

Related papers

• H2PIPE: High throughput CNN Inference on FPGAs with High-Bandwidth Memory [1.0056445773367833]
  Convolutional Neural Networks (CNNs) combine large amounts of parallelizable computation with frequent memory access. This work augments a state-of-the-art dataflow accelerator to leverage both High-Bandwidth Memory (HBM) and on-chip storage. Compared to the best prior work we obtain speed-ups of at least 19.4x, 5.1x and 10.5x on ResNet-18, ResNet-50 and VGG-16 respectively.
  arXiv  Detail & Related papers  (2024-08-17T14:25:32Z)
• Efficient Context Integration through Factorized Pyramidal Learning for Ultra-Lightweight Semantic Segmentation [1.0499611180329804]
  We propose a novel Factorized Pyramidal Learning (FPL) module to aggregate rich contextual information in an efficient manner. We decompose the spatial pyramid into two stages which enables a simple and efficient feature fusion within the module to solve the notorious checkerboard effect. Based on the FPL module and FIR unit, we propose an ultra-lightweight real-time network, called FPLNet, which achieves state-of-the-art accuracy-efficiency trade-off.
  arXiv  Detail & Related papers  (2023-02-23T05:34:51Z)
• SCONNA: A Stochastic Computing Based Optical Accelerator for Ultra-Fast, Energy-Efficient Inference of Integer-Quantized CNNs [0.0]
  A CNN inference task uses convolution operations that are typically transformed into vector-dot-product (VDP) operations. Several photonic microring resonators (MRRs) based hardware architectures have been proposed to accelerate integer-quantized CNNs. Existing photonic MRR-based analog accelerators exhibit a very strong trade-off between the achievable input/weight precision and VDP operation size.
  arXiv  Detail & Related papers  (2023-02-14T13:35:15Z)
• Attention-based Feature Compression for CNN Inference Offloading in Edge Computing [93.67044879636093]
  This paper studies the computational offloading of CNN inference in device-edge co-inference systems. We propose a novel autoencoder-based CNN architecture (AECNN) for effective feature extraction at end-device. Experiments show that AECNN can compress the intermediate data by more than 256x with only about 4% accuracy loss.
  arXiv  Detail & Related papers  (2022-11-24T18:10:01Z)
• Efficient Dataset Distillation Using Random Feature Approximation [109.07737733329019]
  We propose a novel algorithm that uses a random feature approximation (RFA) of the Neural Network Gaussian Process (NNGP) kernel. Our algorithm provides at least a 100-fold speedup over KIP and can run on a single GPU. Our new method, termed an RFA Distillation (RFAD), performs competitively with KIP and other dataset condensation algorithms in accuracy over a range of large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-21T15:56:13Z)
• Toward Efficient Hyperspectral Image Processing inside Camera Pixels [1.6449390849183356]
  Hyperspectral cameras generate a large amount of data due to the presence of hundreds of spectral bands. To mitigate this problem, we propose a form of processing-in-pixel (PIP) Our PIP-optimized custom CNN layers effectively compress the input data, significantly reducing the bandwidth required to transmit the data downstream to the HSI processing unit.
  arXiv  Detail & Related papers  (2022-03-11T01:06:02Z)
• Learning Neural Light Fields with Ray-Space Embedding Networks [51.88457861982689]
  We propose a novel neural light field representation that is compact and directly predicts integrated radiance along rays. Our method achieves state-of-the-art quality on dense forward-facing datasets such as the Stanford Light Field dataset.
  arXiv  Detail & Related papers  (2021-12-02T18:59:51Z)
• Pixel Difference Networks for Efficient Edge Detection [71.03915957914532]
  We propose a lightweight yet effective architecture named Pixel Difference Network (PiDiNet) for efficient edge detection. Extensive experiments on BSDS500, NYUD, and Multicue datasets are provided to demonstrate its effectiveness. A faster version of PiDiNet with less than 0.1M parameters can still achieve comparable performance among state of the arts with 200 FPS.
  arXiv  Detail & Related papers  (2021-08-16T10:42:59Z)
• Real-time Semantic Segmentation with Fast Attention [94.88466483540692]
  We propose a novel architecture for semantic segmentation of high-resolution images and videos in real-time. The proposed architecture relies on our fast spatial attention, which is a simple yet efficient modification of the popular self-attention mechanism. We show that results on multiple datasets demonstrate superior performance with better accuracy and speed compared to existing approaches.
  arXiv  Detail & Related papers  (2020-07-07T22:37:16Z)
• Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes [98.65457534223539]
  We propose a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes. The proposed method achieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU) with the inference speed of 51.0 fps and 39.3 fps.
  arXiv  Detail & Related papers  (2020-03-11T08:45:53Z)
• FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale Context Aggregation and Feature Space Super-resolution [14.226301825772174]
  We introduce a novel and efficient module called Cascaded Factorized Atrous Spatial Pyramid Pooling (CF-ASPP) It is a lightweight cascaded structure for Convolutional Neural Networks (CNNs) to efficiently leverage context information. We achieve 68.4% mIoU at 84 fps on the Cityscapes test set with a single Nivida Titan X (Maxwell) GPU card.
  arXiv  Detail & Related papers  (2020-03-09T03:53:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.