Abstract: A reconfigurable $\mathbf{1 6 K B}$ cache memory system is designed using Verilog Hardware Description Language to support multiple cache mapping techniques, including direct-mapped and ...

Large-scale applications, such as generative AI, recommendation systems, big data, and HPC systems, require large-capacity and high-speed memory and are changing the power-law locality, which ...

TL;DR: Google developed three AI compression algorithms-TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss-that reduce large language models' KV cache memory by at least six times without ...

Running a 70-billion-parameter large language model for 512 concurrent users can consume 512 GB of cache memory alone, nearly four times the memory needed for the model weights themselves. Google on ...

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 ...

TurboQuant compresses AI model vectors from 32 bits down to as few as 3 bits by mapping high-dimensional data onto an efficient quantized grid. (Image: Google Research) The AI industry loves a big ...

Memory-augmented Large Language Models (LLMs) have demonstrated remarkable capability for complex and long-horizon embodied planning. By keeping track of past experiences and environmental states, ...

Less than a week after the United States and Israel launched military strikes on Iran, the conflict has sharply expanded, roping in several Middle Eastern nations and prompting some European countries ...

Enterprise AI applications that handle large documents or long-horizon tasks face a severe memory bottleneck. As the context grows longer, so does the KV cache, the area where the model’s working ...

As AI workloads extend across nearly every technology sector, systems must move more data, use memory more efficiently, and respond more predictably than traditional design methodologies allow. These ...