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In-memory operators for medical image processing

delete2025-06-10
delete0
PRE
AI
H
Hèctor Martínez
J
Juan Gómez-Luna
R
Rafael Palomar
J
Joaquín Olivares
DOI：10.1016/j.future.2025.107939delete
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Abstract

Abstract

En 中文
Medical-image processing (MIP) frequently faces challenges related to computational efficiency and memory bandwidth, primarily due to the intensive data movement between processing units and memory. This work explores the emerging paradigm of Processing-in-Memory (PIM) to alleviate these data movement bottlenecks in MIP. It presents the first PIM implementation of five fundamental algorithms widely used in MIP: voxel-counting, thresholding, histogram computation, convolution, and interpolation, outlining specific PIM patterns. The algorithms, implemented using the UPMEM PIM architecture, were evaluated in real non-commercial PIM hardware (20 DDR4-2400 PIM modules providing 160 GB PIM memory), using both synthetic and real data sets of varying image sizes and underlying datatypes (INT8, INT32, FP32), thus covering a wide range of applications. The evaluation results indicate that, for data-intensive tasks, the PIM prototype can improve significantly the computational efficiency over traditional commercial CPU 20 ×, and GPU 3 ×. This research highlights the potential of PIM for revolutionizing MIP applications by enabling faster and more energy-efficient processing of medical images, thereby addressing critical needs in clinical and research applications.
Keywords:
Medical imaging
Data movement
Processing-in-memory
Near-memory processing

Journal

F
Future Generation Computer Systems
IF:
0
Papers:
642
Citations:
0

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