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

2026-09-07

AI Summary

本文提出了一种名为edgeFBP的框架，通过采用混合精度策略加速反投影内核，解决了边缘设备上因计算能力和内存限制而难以进行CT成像的问题。

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.

Problem

• Computed Tomography
• Edge Devices
• Memory Capacity
• Computational Power
• Energy Budget

Innovation

• out-of-core
• half-precision Tensor Cores
• back-projection kernel
• energy efficiency
• edge devices