The silicon industry has arrived at a consensus that the HBM pathway to unlock incremental AI compute is stalling, especially as converting silicon wafers into an HBM stack yields far less usable capacity than commodity DRAM, prompting the wider industry to start experimenting with SRAM-only decode, Processor-In-Memory (PIM) within LPDDR, CXL pooling, and 3D DRAM. Of course, it is the 3D DRAM architecture that is considered somewhat of a panacea at the moment, as highlighted by AMD's recent admission as to its phenomenal energy efficiency. Even so, odious hurdles continue to prevent the commercialization of this promising technology. AMD has [β¦]
d-Matrix proposes 3D DRAM as a solution to the rising AI data problem using stacked memory to deliver SRAM-Like bandwidth with magnitudes higher efficiency than HBM. The Time For 3D DRAM Is Now, Says d-Matrix, As It Unveils its "Raptor" 3D DRAM Solution With SRAM-Class Bandwidth As AI model sizes and KV cache continue to expand, capacity and bandwidth constraints become more visible. So far, all major DRAM manufacturers are working toward next-gen solutions that stack logic with DRAM, but d-Matrix says "The Time For 3D DRAM" is now. Today, there are two leading memory technologies for AI workloads: SRAM [β¦]