Memory prices are plunging and stocks in memory companies are collapsing following news from Google Research of a breakthrough that will greatly reduce the amount of memory needed for AI processing.
Morning Overview on MSN
30-nm embedded memory could speed AI chips by cutting data shuttling
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, ...
Google's TurboQuant combines PolarQuant with Quantized Johnson-Lindenstrauss correction to shrink memory use, raising ...
Google TurboQuant reduces memory strain while maintaining accuracy across demanding workloads Vector compression reaches new efficiency levels without additional training requirements Key-value cache ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results