一种通用的、可配置的、高效的第一代和第二代离散小波包变换体系结构,具有超高速和低成本的FPGA实现

Mouhamad Chehaitly, M. Tabaa, F. Monteiro, A. Dandache
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引用次数: 0

摘要

这项工作是无线传感器网络领域更广泛项目的一部分,其中小波变换是传输功能的核心。本文的目标是提出一种新的DWT架构,其特点是具有高水平的性能和低成本的设计。这一目标的实现主要得益于DWT算法中不同过滤器之间硬件资源的智能共享。本文介绍了基于Mallat算法的第一代离散小波包变换(DWPT)和基于提升方案的第二代离散小波包变换(DWPT)的体系结构。这些架构使我们能够以高采样率(高达每秒750兆样本)计算DWPT,同时只需要有限的硬件资源,并且在DWPT / IDWPT(逆DWPT)变换的不同深度阶段之间或内部不需要内存存储。这项工作是无线传感器网络领域更广泛项目的一部分,其中小波变换是传输功能的核心。本文的目标是提出一种新的DWT架构,其特点是具有高水平的性能和低成本的设计。这一目标的实现主要得益于DWT算法中不同过滤器之间硬件资源的智能共享。本文介绍了基于Mallat算法的第一代离散小波包变换(DWPT)和基于提升方案的第二代离散小波包变换(DWPT)的体系结构。这些架构使我们能够以高采样率(高达每秒750兆样本)计算DWPT,同时只需要有限的硬件资源,并且在DWPT / IDWPT(逆DWPT)变换的不同深度阶段之间或内部不需要内存存储。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A generic, configurable and efficient architecture for first and second generation discrete wavelet packet transform with ultra-high speed and low-cost FPGA implementation
This work is part of a broader project in the field of wireless sensor networks, in which the wavelet transform is at the core of the transmission functions. Our goal in this paper is to propose a new DWT architecture characterized by a high level of performance and a low cost design. This goal is achieved in particular thanks to the intelligent sharing of hardware resources between the different filters in the DWT algorithm. This paper presents the architectures developped for the first generation Discrete Wavelet Packet Transform (DWPT), based on the Mallat algorithm, and for the second generation DWPT, based on the lifting scheme. These archictures empower us to compute DWPT at high sampling rates (upto 750 Mega-samples per second) while requiring only limited hardware resources and no memory storage between or within the different depth stages of the DWPT / IDWPT (Inverse DWPT) transform.This work is part of a broader project in the field of wireless sensor networks, in which the wavelet transform is at the core of the transmission functions. Our goal in this paper is to propose a new DWT architecture characterized by a high level of performance and a low cost design. This goal is achieved in particular thanks to the intelligent sharing of hardware resources between the different filters in the DWT algorithm. This paper presents the architectures developped for the first generation Discrete Wavelet Packet Transform (DWPT), based on the Mallat algorithm, and for the second generation DWPT, based on the lifting scheme. These archictures empower us to compute DWPT at high sampling rates (upto 750 Mega-samples per second) while requiring only limited hardware resources and no memory storage between or within the different depth stages of the DWPT / IDWPT (Inverse DWPT) transform.
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