Abstract: Sparse general matrix-matrix multiplication is widely used in data mining applications. Its irregular memory access patterns limit the performance of general-purpose processors, thus ...
Abstract: Sparse-sparse matrix multiplication (SpGEMM) is a well-studied problem on CPUs, GPUs, accelerators (e.g. FPGAs), and distributed systems. The main computational bottleneck in SpGEMM is 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.
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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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