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 ...

You're currently following this author! Want to unfollow? Unsubscribe via the link in your email. For the most powerful voices in AI, it's all about being in the loop. Claude Code creator Boris Cherny ...

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

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 ...

Mechanism-level reproduction of Google's Nested Learning (HOPE) architecture (HOPE blocks, CMS, and Self‑Modifying TITANs), matching the quality bar set by lucidrains' TITAN reference while remaining ...