An Efficient Out-of-Core Tomographic Imaging Framework for Edge Devices

Xuetao Chen; Cong Ma; Xiangyu Meng; Du Wu; Zhengyang Bai; Tao Luo; Zhaorui Zhang; Emmanuel Jeannot; Edgar Josafat Martinez Noriega; Xun Wang; Peng Chen; Amelie Chi Zhou; Mohamed Wahib

arXiv · 2609.07249

An Efficient Out-of-Core Tomographic Imaging Framework for Edge Devices

Xuetao Chen·Cong Ma·Xiangyu Meng·Du Wu·Zhengyang Bai·Tao Luo·Zhaorui Zhang·Emmanuel Jeannot·Edgar Josafat Martinez Noriega·Xun Wang·Peng Chen·Amelie Chi Zhou·Mohamed Wahib

View original source ↗Read source document ↗Export metadata ↓

Abstract

Computed Tomography (CT) is an essential 3D imaging technology widely used in medical diagnostics and scientific research. However, performing CT imaging on edge devices is challenging due to limitations in computational power, memory capacity, and energy budget. This paper presents an efficient CT reconstruction framework, called edgeFBP, designed for Nvidia Jetson System-on-Chip (SoC) devices. edgeFBP adopts an end-to-end pipeline design for efficient out-of-core image reconstruction under tight power and memory constraints. edgeFBP utilizes a mixed-precision strategy leveraging half-precision Tensor Cores (TCs) to accelerate the bottleneck back-projection (BP) kernel. edgeFBP achieves a 1.83x speedup over the widely used RTK library on Jetson Nano and a 2.56x speedup on Jetson AGX. Under a strict 25-Watt power budget, edgeFBP on Jetson Nano achieves up to 5-48x higher energy efficiency than an Nvidia DGX A100, enabling datacenter-scale imaging on constrained edge devices.

Explore related subjects

cs.DC

Keep this discovery

BibTeX RIS

Xuetao Chen, Cong Ma, Xiangyu Meng, Du Wu, Zhengyang Bai, Tao Luo, Zhaorui Zhang, Emmanuel Jeannot, Edgar Josafat Martinez Noriega, Xun Wang, Peng Chen, Amelie Chi Zhou, Mohamed Wahib. 2026-09-07. An Efficient Out-of-Core Tomographic Imaging Framework for Edge Devices. https://arxiv.org/abs/2609.07249

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

cs.DC subject

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

arXiv2026

DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space

Existing text-to-image diffusion models excel at generating high-quality images, but face significant efficiency challenges when scaled to high resolutions, like 4K image generation. While previous research accelerates diffusion models in various aspects, it seldom handles the inherent redundancy within the latent space. To bridge this gap, this paper introduces DC-Gen, a general framework that accelerates text-to-image diffusion models by leveraging a deeply compressed latent space. Rather than a costly training-from-scratch approach, DC-Gen uses an efficient post-training pipeline to preserve the quality of the base model. A key challenge in this paradigm is the representation gap between the base model's latent space and a deeply compressed latent space, which can lead to instability during direct fine-tuning. To overcome this, DC-Gen first bridges the representation gap with a lightweight embedding alignment training. Once the latent embeddings are aligned, only a small amount of LoRA fine-tuning is needed to unlock the base model's inherent generation quality. We verify DC-Gen's effectiveness on SANA and FLUX.1-Krea. The resulting DC-Gen-SANA and DC-Gen-FLUX models achieve quality comparable to their base models but with a significant speedup. Specifically, DC-Gen-FLUX reduces the latency of 4K image generation by 53x on the NVIDIA H100 GPU. When combined with NVFP4 SVDQuant, DC-Gen-FLUX generates a 4K image in just 3.5 seconds on a single NVIDIA 5090 GPU, achieving a total latency reduction of 138x compared to the base FLUX.1-Krea model. Code: https://github.com/dc-ai-projects/DC-Gen.

cs.CV↗

arXiv2026

On Reduction and Synthesis of Petri's Cycloids

Cycloids are particular Petri nets for modelling processes of actions and events, belonging to the fundaments of Petri's general systems theory. Defined by four parameters they provide an algebraic formalism to describe strongly synchronized sequential processes. To further investigate their structure, reduction systems of cycloids are defined in the style of rewriting systems and properties of irreducible cycloids are proved. In particular the synthesis of cycloid parameters from their Petri net structure is derived, leading to an efficient method for a decision procedure for cycloid isomorphism.

cs.DC↗

arXiv2026

On Solving Problems of Substantially Super-linear Complexity in $N^{o(1)}$ Rounds in the MPC Model

We study the possibility of designing $N^{o(1)}$-round protocols for problems of substantially super-linear polynomial-time (sequential) complexity in the model of Massively Parallel Computation, where $N$ is the input size. We show that if the machines are not equipped with relatively large local memory and their number does not exceed $N$, then the exponent of the average time complexity of the local computation performed by a machine in a round (in terms of local memory size) in such protocols must be larger than the exponent of the time complexity of the given problem.

cs.DC↗