London, 27 March 2024: Oriole Networks – a startup using light to train LLMs faster with less power – has raised £10 million in seed funding to improve AI performance and adoption, and solve AI’s energy problem. The round, which the company said is one of the UK’s largest seed raises in recent years, was co-led […]

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oneAPI offers an open industry effort supported by over 100 organizations. oneAPI is an open, unified, cross-architecture programming model for CPUs and accelerator architectures (GPUs, FPGAs, and others). Based on standards, the programming model simplifies software development and delivers uncompromised performance for accelerated compute without proprietary lock-in, while enabling the integration of existing code.

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Researchers at Los Alamos National Laboratory today announced a quantum machine learning “proof” they say shows that training a quantum neural network requires only a small amount of data, “(upending) previous assumptions stemming from classical computing’s huge appetite for data in machine learning, or artificial intelligence.” The lab said the theorem has direct applications, including […]

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Open engineering consortium MLCommons has released new results from MLPerf Training v2.0, which measures how fast various platforms train machine learning models. The organizations said the latest MLPerf Training results “demonstrate broad industry participation and up to 1.8X greater performance ultimately paving the way for more capable intelligent systems….” As it has done with previous […]

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In this sponsored post, Tim Miller, Vice President, Product Marketing, One Stop Systems, discusses autonomous trucking and that to achieve AI Level 4 (no driver) in the vehicles, powerful AI inference hardware supporting many different inferencing engines operating and coordinating simultaneously is required.

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Reports are circulating in AI circles that researchers from Rice University claim a breakthrough in AI model training acceleration – without using accelerators. Running AI software on commodity x86 CPUs, the Rice computer science team say  neural networks can be trained 15x faster than platforms utilizing GPUs. If valid, the new approach would be a double boon for organizations implementing AI strategies: faster model training using less costly microprocessors.

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