With the growing model size of deep neural networks (DNN), deep learning training is increasingly relying on handcrafted search spaces to find efficient parallelization execution plans. However, our ...
When it comes to AI, many enterprises seem to be stuck in the prototype phase. Teams can be constrained by GPU capacity and complex and opaque model workflows; or, they don’t know when enough training ...
What if the key to unlocking faster, more efficient AI development wasn’t just in the algorithms you write, but in the hardware you choose? For years, the debate between Google’s Tensor Processing ...
The rise of large language models (LLMs) has transformed natural language processing, but training these models comes with significant challenges. Training state-of-the-art models like GPT and Llama ...
Abstract: Split Federated Learning (SFL) improves scalability of Split Learning (SL) by enabling parallel computing of the learning tasks on multiple clients. However, state-of-the-art SFL schemes ...
Imagine a world where the wait for your 3D-printed masterpiece shrinks from a day and a half to just a few hours. It sounds like a dream, right? If you’ve ever felt the frustration of watching your ...
Abstract: Obtaining clear sonar images is crucial for ocean exploration applications, such as marine resource detection and underwater target searches. Traditional filtering methods cannot effectively ...
Monad Labs raised $225 million, led by Paradigm, pushing forward the discussion on parallelized EVM chains. Monad is a new layer-1 smart contract platform that recently raised $225 million in funding ...
A team of researchers in Japan released Fugaku-LLM, a large language model [1] with enhanced Japanese language capability, using the RIKEN supercomputer Fugaku. The team is led by Professor Rio Yokota ...
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