来源：内容编译自semianalysis。 在人工智能和深度学习领域，GPU计算能力的提升速度远超摩尔定律，年复一年地持续实现着“黄氏定律”般显著的性能提升。推动这一进步的核心技术正是 Tensor Core。 尽管 Tensor Core 无疑是现代人工智能和机器学习的基石，但即使是 ...

Tensor Core 是用于加速深度学习计算的关键技术，其主要功能是执行神经网络中的矩阵乘法和卷积运算。通过利用混合精度计算和张量核心操作，Tensor Core 能够在较短的时间内完成大量矩阵运算，从而显著加快神经网络模型的训练和推断过程。具体来说，Tensor Core ...

Mathematicians love a good puzzle. Even something as abstract as multiplying matrices (two-dimensional tables of numbers) can feel like a game when you try to find the most efficient way to do it.

Algorithms have been used throughout the world’s civilizations to perform fundamental operations for thousands of years. However, discovering algorithms is highly challenging. Matrix multiplication is ...

Multiplying the content of two x-y matrices together for screen rendering and AI processing. Matrix multiplication provides a series of fast multiply and add operations in parallel, and it is built ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

A recent paper set the fastest record for multiplying two matrices. But it also marks the end of the line for a method researchers have relied on for decades to make improvements. For computer ...

Over at the NVIDIA blog, Loyd Case shares some recent advancements that deliver dramatic performance gains on GPUs to the AI community. We have achieved record-setting ResNet-50 performance for a ...

Familiarity with linear algebra is expected. In addition, students should have taken a proof-based course such as CS 212 or Math 300. Tensors, or multiindexed arrays, generalize matrices (two ...