Storage Bottlenecks in the AI Era and the Path to Technological Breakthroughs
Traditional Storage Architectures Hamper AI Development, with Power Consumption and Performance Conflicts Highlighting
As demands for edge AI and data center deep learning explode, the limitations of traditional storage technologies (SRAM, LPDDR, HBM) have become increasingly evident: SRAM suffers from high leakage power and poor scalability for large discrete storage chips; LPDDR has latency and energy efficiency issues; HBM, despite high bandwidth, consumes significant power. Among them, DRAM power consumption accounts for over 30% of data center total power usage, with refresh cycles and workload fluctuations further exacerbating energy pressure, becoming a “fatal weakness” for AI development.
Emerging Technologies and Architectural Innovations Outline the Future Vision
Ideal AI storage requires ultra-fast read/write speeds, high bandwidth, ultra-low power consumption, and scalability. Technologies like Magnetoresistive RAM (MRAM), Resistive RAM (RRAM), 3D DRAM, and Computing in Memory (CIM)/Processing in Memory (PIM) are becoming key to breaking through bottlenecks. Additionally, ecosystem coordination, chip stacking technology upgrades, and SoC layout optimization will help release potential for AI performance enhancement.