Abstract: Many-objective optimization problems are challenging and time-consuming, especially when the number of objectives is large. In this paper, an evolutionary algorithm inspired by quantum ...

The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” ...

If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what ...

As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

Abstract: Evolutionary algorithms (EAs) exhibit diverse performance across different optimization problems, making the automated selection of the most suitable EA an important yet challenging task.