AI will generate increased demand for memory. This will drive demand for HBM memory but also increase demand for new emerging memory technologies.
Energy & Commerce Committee Subcommittee of Communication and Technology, Ronnie Vasishta, Senior VP of telecom at Nvidia said that mobile networks will be called upon to support a new kind of traffic—AI traffic. This AI traffic includes the delivery of AI services to the edge, or inferencing at the edge. Such growth in AI data could reverse the general trend towards lower growth in traffic on mobile networks.
Micron announced that it is now shipping HBM4 memory to key customers, these are for early qualification efforts. The Micron HBM4 provides up to 2.0TB/s bandwidth and 24GB capacity per 12-high die stack. The company says that their HBM4 uses its 1-beta DRAM node, advanced through silicon via technologies, and has a highly capable built-in self-test. See image below.
The company says that its AI Memory Engine is a highly configurable memory subsystem IP that enables significant improvements in power efficiency, performance, intelligence, and endurance. This is not only for Numem’s MRAM-based architecture, but also third-party MRAMs, RRAM, PCRAM, and Flash Memory.
Hbm4 Mram Rram Reram Fram Pcm Ai Inference Training
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