Abstract: The demand for high-speed matrix multiplication continues to grow due to recent developments in images processing, graphics processing, digital signal processing and communication via ...

Abstract: Graph convolutional networks (GCNs) are emerging neural network models designed to process graph-structured data. Due to massively parallel computations using irregular data structures by ...

This project investigates how different multithreaded matrix multiplication strategies affect performance. The objective was to implement parallel matrix multiplication to explore how thread count, ...

At 2:00 AM in the lab, when an Excel graph crashed due to an unknown error, I once looked at my classmate next to me and we laughed, saying, "Isn't it about time we just went to sleep?" Many KOSEN ...

D-Matrix says its chips can run inference workloads 10 times faster and using five times less energy than a standalone graphics processing unit from Nvidia. Like Cerebras, D-Matrix is trying to prove ...