High throughput spatial convolution filters on FPGAs

Digital signal processing (DSP) on field- programmable gate arrays (FPGAs) has long been appealing because of the inherent parallelism in these computations that can be easily exploited to accelerate such algorithms. FPGAs have evolved significantly to further enhance the mapping of these algorithms, included additional hard blocks, such as the DSP blocks found in modern FPGAs. Although these DSP blocks can offer more efficient mapping of DSP computations, they are primarily designed for 1-D filter structures. We present a study on spatial convolutional filter implementations on FPGAs, optimizing around the structure of the DSP blocks to offer high throughput while maintaining the coefficient flexibility that other published architectures usually sacrifice. We show that it is possible to implement large filters for large 4K resolution image frames at frame rates of 30–60 FPS, while maintaining functional flexibility

A study about FPGA-based digital filters

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. Valls, M. M. Peiró, T. Sansaloni, and E. Boemo, "A study about FPGA-based digital filters", in IEEE Workshop on Signal Processing Systems, 1998, p. 192-201A set of operators suitable for digit-serial FIR filtering is presented. The canonical and inverted forms are studied. In each of these structures both the symmetrical and anti-symmetrical particular cases are also covered. All circuits have been implemented using an EPF10K50 Altera FPGA. The main results show that the canonical form presents less occupation and higher throughput. The 8-tap filter versions implemented can be applied in real-time processing with sample rate ranging up to 7 MHz using the bit-serial versions and up to 25 MHz with the bit-parallel one

Design and Implementation of Parallel FIR Filter Using High Speed Vedic Multiplier

The demand for high speed processing has been increasing as a result of expanding computer and signal processing applications. Higher throughput arithmetic operations are important to achieve the desired performance in many signal processing and image processing applications. One of the key arithmetic operations in such applications is multiplication which determines the performance of the entire system. Thus the optimization of the multiplier speed and area is a challenge for many processors. This challenge has been successfully overcome by the use of ancient Vedic multiplier. This paper illustrates design and implementation of parallel Finite Impulse Response (FIR) filters using Vedic mathematics based Urdhva Tiryabhyam algorithm. The system is aiming to reduced propagation delay and area of the filter. The proposed system based on Vedic multiplier is compared with that on conventional multiplier on the basis of resources and time required for processing given data. The comparison shows the 36.29% and 15.70% reduction in propagation delay for two-parallel and three-parallel FIR filter using Vedic multiplier as compared to that of conventional multiplier. The architecture is coded in VHDL and synthesized and simulated by using Xilinx Design Suite 13.1 ISE

Structured Parallel Architecture for Displacement MIMO Kalman Equalizer in CDMA Systems

A reduced complexity MIMO Kalman equalizer architecture is proposed in this brief by jointly considering the displacement structure and the block-Toeplitz structure. Numerical matrix–matrix multiplications with O(F3) complexity are eliminated by simple data loading process, where is the spreading factor. Finally, an iterative Conjugate-Gradient based algorithm is proposed to avoid the inverse of the Hermitian symmetric innovation covariance matrix in Kalman gain processor. The proposed architecture not only reduces the numerical complexity from O(F2) to O(Flog2F) per chip, but also facilitates the parallel and pipelined VLSI implementation in real-time processing