All in on MatMul? Don’t Put All Your Tensors in One Basket!
Matrix multiplication dominates AI hardware and research. Betting everything on MatMul risks an innovation monoculture — it’s time to diversify our compute bets.
Archive of posts tagged: Machine Learning
A Computer Architect’s Guide to Designing Abstractions for Intelligent Systems
“The growing complexity of intelligent systems can outpace the ability of conventional computing abstractions to support them effectively.” IET REACH 2024, Amir Yazdanbakhsh. I came across this observation by Edsger Dijkstra recently while scrolling X, and...
The QuArch Experiment: Crowdsourcing a Machine Learning Dataset for Computer Architecture
Introduction The rise of large language models (LLMs) and generative artificial intelligence (GenAI) presents new opportunities to build innovative tools and is already enabling revolutionary AI-based tools in various domains. However, a significant gap remains in the...
Efficient GEMM Kernel Designs with Pipelining
General Matrix Multiplication (GEMM) is a fundamental operation in machine learning and scientific computing. It is the classic example of an algorithm that benefits greatly from GPU acceleration due to its high degree of data parallelism. More recently, efficient...
TinyML: Why the Future of Machine Learning is Tiny and Bright
At the dawn of the 21st century, Mark Weiser envisioned a world where computers would weave themselves into the fabric of everyday life, becoming indistinguishable from it. This prophecy of ubiquitous computing has not only materialized but has evolved beyond...Subscribe
