AMD and Intel have now published a full technical specification for ACE — AI Compute Extensions — the most significant overhaul to x86 AI compute in the architecture's history, co-authored by eight ...

Running AI models on x86 CPUs is becoming easier and faster ...

Abstract: Large-scale matrix multiplication is a computational bottleneck in various applications including artificial intelligence and machine learning. Given the time complexity of O(n 3) for matrix ...

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 ...

Abstract: Artificial Intelligence (AI) has permeated various domains but is limited by the bottlenecks imposed by data transfer latency inherent in contemporary memory technologies. Matrix ...

In an age of complex programming languages and application packages that ship with massive storage requirements, it’s easy to forget what pure assembly—low-level code that passes direct instructions ...

It’s been three-and-a-half years since generative AI exploded onto the scene. In this past year, progress has continued its relentless pace: Vibe coding took off, companies embraced agentic workflows, ...

The Fortran programming language originated in the 1950s. Today it runs software for heavy numeric computation and high-performance computing. It was the 11th most popular programming language on the ...