As generative AI models get more sophisticated, companies need more memory and faster memory, Micron CEO Sanjay Mehrotra said in January.

Nvidia's KV Cache Transform Coding (KVTC) compresses LLM key-value cache by 20x without model changes, cutting GPU memory costs and time-to-first-token by up to 8x for multi-turn AI applications.

The AI hardware boom is sending memory prices sky-high, so knowing exactly how much you need is more critical than ever. I've worked out the most realistic RAM goals for every type of PC. I’ve been a ...

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), ...

It's that point in the hardware cycle where it's worth taking a fresh look at how much system memory modern games actually use – and, more importantly, how much you really need. With DRAM pricing ...

The TCP/IP functionality of a connected device uses dynamic RAM allocation because of the unpredictable nature of network behavior. For example, if a device serves a web dashboard, we cannot control ...

Abstract: In wideband wireless communication systems, it is particularly crucial to compensate for the memory effect of radio frequency power amplifiers (RFPAs) to ensure optimal system performance.

What if your AI could remember not just what you told it five minutes ago, but also the intricate details of a project you started months back, or even adapt its memory to fit the shifting needs of a ...

LWMalloc is an ultra-lightweight dynamic memory allocator designed for embedded systems that is said to outperform ptmalloc used in Glibc, achieving up to 53% faster execution time and 23% lower ...

Abstract: The lag consensus problem of multi-uncrewed aerial vehicle (UAV) systems under hybrid attacks is investigated in this paper. First, a dynamic memory event-triggered control protocol is ...