Sparse general matrix-matrix multiplication (SpGEMM) is fundamental to numerous scientific applications. Traditional hash-based approaches fail to strike a trade-off between reducing hash collisions ...

This code accompanies the blog post Matrix Multiplication Faster Than Nvidia, Sometimes. It provides a CUDA kernel for single-precision matrix-matrix multiplication, with two notable features: use of ...

Nearly all big science, machine learning, neural network, and machine vision applications employ algorithms that involve large matrix-matrix multiplication. But multiplying large matrices pushes the ...

Abstract: We propose a hyperspectral compute-in-memory architecture using optical frequency combs and programmable optical memories. By fully utilizing frequency, space, and time dimensions, this ...

Abstract: Efficiently synthesizing an entire application that consists of multiple algorithms for hardware implementation is a very difficult and unsolved problem. One of the main challenges is the ...