Communication Dans Un Congrès Année : 2025

Comparative Study of Memory Optimization Techniques for Dataflow-Modeled Applications

Résumé

Efficient memory management is essential for signal and image processing systems, particularly in data-intensive applications where performance and resource constraints are critical. This paper presents a comparative study of two advanced memory optimization techniques: Memory Script Optimization (MSO), and Passive Active Flow Graph (PAFG) Optimization-within the context of dataflow-modeled applications. Both approaches aim to reduce memory usage and improve execution efficiency, but they do so with distinct strategies: Memory Scripts focus on in-place buffer management, while PAFG modifies actor interactions to minimize buffer requirements. Using a portion of a Convolutional neural network (CNN) application as a case study, we evaluate the efficiency of these techniques in terms of memory reduction and execution time. Our results demonstrate that MSO provides significant performance improvements, achieving up to 17% memory savings and 21% faster execution times, making it ideal for independent data operations. However, PAFG offers greater scalability and flexibility, particularly when dealing with complex data dependencies, and provides a simpler path to implementation. This work not only highlights the tradeoffs between memory efficiency and flexibility but also paves the way for applying these optimizations in near-memory computing architectures, where distance from memory to processing is employed as a parameter to improve efficiency.

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Dates et versions

hal-05117283 , version 1 (17-06-2025)

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Identifiants

  • HAL Id : hal-05117283 , version 1

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Naouel Haggui, Maxime Pelcat, Yaesop Lee, Shuvra S. Bhattacharyya, Kevin Martin, et al.. Comparative Study of Memory Optimization Techniques for Dataflow-Modeled Applications. DASIP 2025, Jan 2025, Barcelona (ES), Spain. ⟨hal-05117283⟩
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