Transformations are the key to such codes, and they rely on math that predates computing as we know it by centuries. There ...

ThunderKittens is a framework to make it easy to write fast deep learning kernels in CUDA. It is built around three key principles: ThunderKittens is built from the hardware up; we do what the silicon ...

Triton is a language and compiler for writing highly efficient ML primitives, one of the most common primitive is matrix-multiplication. Triton typically builds these primitives using just-in-time ...

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Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing.

Two manipulator Jacobian matrix estimators for constrained planar snake robots are developed and tested, which enables the implementation of Jacobian-based obstacle-aided locomotion (OAL) control ...

Matrix computation, as a fundamental building block of information processing in science and technology, contributes most of the computational overheads in modern signal processing and artificial ...

Semantic segmentation, which is the fundamental and challenging problem in computer vision, is to parse the category of each pixel in the image. It has been extensively researched in a variety of ...

Hardware architectures composed of resistive cross-point device arrays can provide significant power and speed benefits for deep neural network training workloads using stochastic gradient descent ...