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.
This repository contains the RTL design, verification environment, and synthesis scripts for a high-performance 8x8 Systolic Array Matrix Multiplier. Designed from a top-down approach, the accelerator ...
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