Abstract: Matrix computation is ubiquitous in modern scientific and engineering fields. Due to the high computational complexity in conventional digital computers, matrix computation represents a ...

Abstract: In-memory computing is an emerging computing paradigm that overcomes the limitations of exiting Von-Neumann computing architectures such as the memory-wall bottleneck. In such paradigm, the ...

Precision has long been the central bottleneck of analogue computing. Bit-slicing or analogue compensation can be used to perform matrix–vector multiplication with precision, but solving matrix ...

Everything on a computer is at its core a binary number, since computers do everything with bits that represent 0 and 1. In order to have a file that is "plain text", so human readable with minimal ...

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 ...

This tutorial shows step-by-step how to create an R and Python package which performs a naive matrix multiplication which uses just three for-loops. However, at each step more and more efficient low ...

Memristor crossbars offer reconfigurable non-volatile resistance states and could remove the speed and energy efficiency bottleneck in vector-matrix multiplication, a core computing task in signal and ...

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of ...

This tutorial presents several misconceptions related to the use the General Linear Model (GLM) in functional Magnetic Resonance Imaging (fMRI). The goal is not to present mathematical proofs but to ...