“Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks and can yield ...
This guide shows how TPUs crush performance bottlenecks, reduce training time, and offer immense scalability via Google Cloud ...
Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large, ...
A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...
Nearly all big science, machine learning, neural network, and machine vision applications employ algorithms that involve large matrix-matrix multiplication. But multiplying large matrices pushes the ...
Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network operations ...
Matrix multiplication is at the heart of many machine learning breakthroughs, and it just got faster—twice. Last week, DeepMind announced it discovered a more efficient way to perform matrix ...
A new RISC-V Tensor Unit, based on fully customizable 64-bit cores, claims to provide a huge performance boost for artificial intelligence (AI) applications compared to just running software on scalar ...
The ARMv8 architecture was announced in 2011, a full decade ago. It was a massive change as it moved from 32-bit to 64-bit. Over the last 5 years there have been more than 100 billion ARMv8 devices.
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