SparsePixels: Efficient Convolution for Sparse Data on FPGAs

summary

The gist

The gist The SparsePixels framework introduces a method for efficient convolution on FPGAs by selectively computing only on active input pixels, which reduces per-layer compute from O(HWK2CinCout) to

In short

SparsePixels introduces a framework for efficient convolution on FPGAs by computing only on active input pixels in spatially sparse data. This method reduces computation from $O(HWK^2C_{ ext{in}}C_{ ext{out}})$ to $O(n^2C_{ ext{in}}C_{ ext{out}})$ and storage from $O(HWC)$ to $O(nC)$. The result is a constant, data-independent inference latency, achieving significant speedups, such as a 73x improvement in latency for neutrino interaction images on an FPGA.

Key concepts

Sparse Convolution: A method where the convolution operation is only performed on the subset of input pixels that contain meaningful information. Instead of scanning every pixel in the input image, this technique selectively retains and computes only on active pixels, ignoring empty regions. This drastically reduces computational load when semantic data is spatially sparse.
Input Reduction Layer: The initial layer in SparsePixels that dynamically identifies and retains a small subset of active pixels from the large input data. It uses a recursive binary reduction scheme to read out these active pixels into two compact arrays: one for features (afeat) and one for coordinates (ahash). This avoids data-dependent write addresses.
Data-Independent Latency: A crucial property where the inference time on the FPGA does not change based on the actual input data. Because computation is fixed to a pre-specified pixel budget frozen during synthesis, the runtime latency remains constant. This makes it ideal for real-time systems with strict microsecond latency requirements.
HLS Implementation Focus: The Hardware Description Language Synthesis (HLS) implementation concentrates on storing only the active pixels in compact arrays and performing all subsequent computations exclusively on these retained elements. The convolution layer then checks coordinates against the hash array to determine if a pixel is within the receptive field, avoiding unnecessary loops over every possible kernel position.

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This episode discusses

The paper

SparsePixels: Efficient Convolution for Sparse Data on FPGAs · Read on arXiv

Ho Fung Tsoi, Dylan Rankin, Vladimir Loncar, Philip Harris

University of Pennsylvania · Institute of Physics Belgrade, Serbia · Massachusetts Institute of Technology, USA · Institute for Artificial Intelligence and Fundamental Interactions, USA

Inference of standard convolutional neural networks (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. However, input features can be spatially sparse in some image data, where semantic information may occupy only a small fraction of the pixels and most computation would be wasted on empty regions. In this work, we introduce SparsePixels, a framework that implements sparse convolution on FPGAs by selectively retaining and computing on a small subset of active input pixels while ignoring the rest, reducing the per-layer compute from O(HWK 2C inC out) to O(n 2C inC out) and the storage from O(HWC) to O(nC) when n squared HW. Because computation always runs over a single pre-specified pixel budget that is frozen at synthesis, the inference latency is data-independent and constant at runtime. We show that, for identifying neutrino interactions in naturally sparse LArTPC images with 4k pixels, 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, achieves a times 73 speedup to 0.665 μ s with resource utilization well within on-chip budgets, trading only a small percent-level performance loss. This work aims to benefit future algorithm development for efficient data readout in modern experiments with strict latency requirements of microseconds or below.

DOI: 10.1088/2632-2153/ae9fb5

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Hi, I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: We're looking at the paper: "SparsePixels: Efficient Convolution for Sparse Data on FPGAs".

Jane: Here's the gist of the paper. The gist The SparsePixels framework introduces a method for efficient convolution on FPGAs by selectively computing only on active input pixels, which reduces per-layer compute from O(HWK2CinCout) to O(n 2CinCout) and storage from O(HW C) to O(nC), achieving significant speedups for spatially sparse data in low-latency applications Problem Statement The core problem addressed is whether it is possible to improve the processing efficiency of CNNs for spatially sparse images on FPGAs to allow for accelerated real-time inference in experiments that require microsecond-level latency or below.

Tom: This challenge arises because standard convolution requires scanning the entire input, which is inefficient when semantic information occupies only a small fraction of the pixels.

Jane: Contributions The authors make three primary contributions to this area.

Paper summary: Tom: So, to wrap up, SparsePixels tackles the high latency of standard CNNs on FPGAs caused by dense convolution when dealing with sparse image data. They introduce this sparse convolution method that drastically cuts down computation and storage requirements when n squared is much less than HW <ref:2512.06208#pg1>.

Jane: It’s about making inference latency data-independent and constant at runtime, which is vital for trigger systems in particle experiments where you need processing to happen in microseconds or below <ref:2512.06208#pg3>.

Lu: The authors provide a Python library for quantization-aware training of sparse CNNs and C++ HLS modules for FPGA deployment, which makes it accessible for deploying these models thirty-one thirty-three <ref:2512.06208#pg3>.

Meng: From a production standpoint, the paper shows that this approach can deliver significant speedups compared to standard CNNs in those low-latency scenarios <ref:2512.06208#pg1>.

Tom: The title, SparsePixels: Efficient Convolution for Sparse Data on FPGAs, points directly at solving the problem of inefficient computation when the input features are spatially sparse <ref:2512.06208#pg1>.

Jane: It gives us a concrete way to think about how to build faster readout systems in modern experiments where microsecond latency is a strict requirement <ref:2512.06208#pg3>.

Conclusion: Tom: So we've been looking at SparsePixels, which is about making convolution faster on FPGAs when the image data isn't really dense.

Jane: Right, so they’re talking about this framework that lets you skip over all the empty pixels in an image instead of processing them all.

Lu: The core idea here is to reduce that computation from something like O of H W squared times K by using a much smaller number, n squared.

Meng: But how does it actually work on hardware? We're talking about real deployment here, not just theoretical math.

Tom: Well, they do this by picking a small subset of active pixels and only computing on those, which cuts down the storage from something like H W C to O of n C.

Jane: That means the memory footprint shrinks a lot when you have sparse data. What’s really interesting is that the latency becomes constant at runtime, not depending on how much data you feed it in.

Lalam: I see what they mean here, it's about making the processing time predictable for real-time systems.

Tom: Exactly, because those fixed latencies are crucial for things like particle detectors where you need a trigger decision in microseconds.

Jane: It’s pretty neat because they showed this on a 4k pixel image of neutrino interactions, and they got a seven-thirty times speedup over the standard CNN.

Lu: That seven-thirty times improvement is substantial, even with that small percent drop in accuracy. It shows the concept works practically for sparse data too.

Meng: I'm interested in what this means for us running things at scale; if we can get this kind of speedup while staying within the chip budget, that opens up a lot of possibilities.

Tom: Yeah, so it’s not just a theoretical trick; it’s about making those high-throughput systems feasible with these sparse inputs.

Jane: It really shows how much efficiency we can gain by being smart about where we spend our compute resources. So, what does this mean for the next steps in building these kinds of fast experimental tools?

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