Abstract: Matrix multiplication is fundamental to deep learning, scientific computation, and graph analytics. The prevalence of sparse matrices in these fields offers significant opportunities to ...

Abstract: Sparse matrix-vector multiplication (SpMV) serves as a crucial operation for several key application domains, such as graph analytics and scientific computing, in the era of big data. The ...

This project focused on developing and implementing two System-on-Chip (SoC) accelerators for performance-critical applications: Matrix Multiplication (MatMul) and Deep Neural Network (DNN) Inference.

A parameterized systolic-array matrix-multiply accelerator in SystemVerilog. Implements a weight-stationary dataflow across an NxN grid of pipelined multiply-accumulate (MAC) units, with a control FSM ...

50-year-old hardware is slow, but rest assured, there are hobbyists out there trying to speed things up by building new era-appropriate accelerator cards. A prime example is @bradthx (Brad) on X, a ...