“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 ...
Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large, ...
There has been an ever-growing demand for artificial intelligence and fifth-generation communications globally, resulting in very large computing power and memory requirements. The slowing down or ...
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 ...
Driven by ever more complex algorithms and more and more channels for the processor to handle, the need for more digital signal processing speed is escalating rapidly. Conventional DSPs are pushing ...
LAS VEGAS, December 03, 2024--(BUSINESS WIRE)--Tachyum ® today announced that it has successfully validated integer matrix operations running on its Prodigy ® Universal Processor FPGA hardware. The ...
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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