This repository includes a pure Vitis HLS implementation of matrix-matrix multiplication (A*B=C) for Xilinx FPGAs, using Xilinx Vitis to instantiate memory and PCIe controllers and interface with the host.

Abstract—This paper describes an FPGA design that performs 4x4 matrix multiplication. The goal of the design is to optimize throughput, area, and accuracy. The design of our matrix multiplier consists of four main parts: fractional binary numbers (fixed point notation), binary multiplication, matrix addition, and fetch routine.

Feb 20, 2010 · In this paper, we present the design and Field Programmable Gate Array (FPGA) implementation of matrix multiplier architectures for use in image and signal processing applications. The...

Oct 1, 2016 · Implementing a large matrix-matrix multiplication on FPGA Approach Using divide-and-conquer techniques to describe the matrix multiplication algorithm and then using SDSoC for high-level synthesis

In this chapter, we take it a step further, demonstrating how to design an efficient matrix multiplication accelerator by improving computational structures, optimizing data access, and enhancing parallelism. Matrix blocking enables better utilization of on-chip memory resources on FPGAs by partitioning large matrices into smaller blocks.

This VHDL project is aimed to develop and implement a synthesizable matrix multiplier core, which is able to perform matrix calculation for matrices with the size of 32x32. Each component of the matrices is 16-bit unsigned integer. The core is implemented on Xilinx FPGA Spartan-6 XC6SLX45-CSG324-3.

This project demonstrates accelerated matrix multiplication on FPGAs; it stores two 3x3 matrices on a microcontroller, and serially streams them to an FPGA which multiplies them and visualizes them along with their product matrix. For a detailed writeup, see report.pdf.

This article presents an efficient and robust method for the field-programmable gate array (FPGA) calculation of the matrix inversion. We initially consider the scenario that the matrix to be processed is a nonsingular Hermitian matrix.

FPGAs are a perfect platform for computationally intensive arithmetic calculations like matrix inversion as they provide powerful computational architectural features: vast amounts of programmable logic elements, embedded multipliers, shift register LUTs (SRLs), Block. RAMs (BRAMs), DSP blocks and Digital Clock Managers (DCMs).

Jan 1, 2020 · In this paper, design and evaluation of different floating-point matrix multiplication architectures/approaches and floating-point matrix inversion using model based design for FPGAs is proposed, which is an outcome of a funded project. Details about the scaling of proposed work for larger matrix sizes is also reported in the paper.