Sharif Digital Repository

A wide range of applications in engineering and scientific computing are based on the sparse matrix computation. There exist a variety of data representations to keep the non-zero elements in sparse matrices, and each representation favors some matrices while not working well for some others. The existing studies tend to process all types of applications, e.g., the most popular application which is matrix–vector multiplication, with different sparse matrix structures using a fixed representation. While Graphics Processing Units (GPUs) have evolved into a very attractive platform for general purpose computations, most of the existing works on sparse matrix–vector multiplication (SpMV, for... 

A wide range of applications in engineering and scientific computing are based on the sparse matrix computation. There exist a variety of data representations to keep the non-zero elements in sparse matrices, and each representation favors some matrices while not working well for some others. The existing studies tend to process all types of applications, e.g., the most popular application which is matrix–vector multiplication, with different sparse matrix structures using a fixed representation. While Graphics Processing Units (GPUs) have evolved into a very attractive platform for general purpose computations, most of the existing works on sparse matrix–vector multiplication (SpMV, for... 

In this work we present a high-throughput, low-complexity design for linear precoding in massive Multiple-Input Multiple-Output (MIMO) systems. Large number of Base Station (BS) antennas in massive MIMO at one hand has caused the performance of linear precoders to be near-optimal and at the other hand has increased their complexity due to the need for the inversion of matrices with large dimensions. To avoid this complexity, approximate inversion methods based on Neumann Series have been proposed. However, in this work, we propose an architecture for Zero Forcing (ZF) precoder based on the Neumann Series approximate inversion that further reduces the complexity by replacing matrix-matrix... 

An integrated photonic neural network is proposed based on on-chip cascaded one-dimensional (1D) metasurfaces. High-contrast transmitarray metasurfaces, termed as metalines in this paper, are defined sequentially in the silicon-on-insulator substrate with a distance much larger than the operation wavelength. Matrix-vector multiplications can be accomplished in parallel and with low energy consumption due to intrinsic parallelism and low-loss of silicon metalines. The proposed on-chip whole-passive fully-optical meta-neural-network is very compact and works at the speed of light, with very low energy consumption. Various complex functions that are performed by digital neural networks can be...