Deep learning and artificial intelligence have been huge topics of interest in 2016, but so far most of the excitement has focused on either Nvidia GPUs or custom silicon hardware like Google's ...

What if you could train massive machine learning models in half the time without compromising performance? For researchers and developers tackling the ever-growing complexity of AI, this isn’t just a ...

Hardware fragmentation remains a persistent bottleneck for deep learning engineers seeking consistent performance.

SE: There seems to be a parallel growth between the adoption of machine learning and GPUs. Is the need for machine learning driving GPU adoption, or are GPUs creating the opportunity to embrace ...

Alongside text-based large language models (LLMs), including ChatGPT in industrial fields, GNN (Graph Neural Network)-based graph AI models that analyze unstructured data such as financial ...

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 ...