Most of the energy an AI chip burns never goes toward actual computation. It goes toward moving data: shuttling model weights ...

In modern CPU device operation, 80% to 90% of energy consumption and timing delays are caused by the movement of data between the CPU and off-chip memory. To alleviate this performance concern, ...

Researchers at Nvidia have developed a technique that can reduce the memory costs of large language model reasoning by up to eight times. Their technique, called dynamic memory sparsification (DMS), ...

Google's TurboQuant combines PolarQuant with Quantized Johnson-Lindenstrauss correction to shrink memory use, raising ...

Researchers have developed a new type of optical memory called a programmable photonic latch that is fast and scalable, enabling temporary data storage in optical processing systems and offering a ...

Google TurboQuant reduces memory strain while maintaining accuracy across demanding workloads Vector compression reaches new efficiency levels without additional training requirements Key-value cache ...