SRAM是什么？和HBM有何不同？

Core Viewpoint - Nvidia's investment of $20 billion in acquiring Groq's Language Processing Unit (LPU) technology highlights the rising importance of SRAM in the AI, server, and high-performance computing (HPC) sectors, shifting the focus from mere capacity to speed, latency, and energy consumption [1][5]. Group 1: SRAM and HBM Comparison - SRAM (Static Random Access Memory) is characterized by high speed and low latency, commonly used within CPUs, GPUs, and AI chips. It is volatile, meaning data is lost when power is off, and it does not require refreshing, making it suitable for immediate data processing [3][4]. - HBM (High Bandwidth Memory) is an advanced type of DRAM that utilizes 3D stacking and through-silicon vias (TSV) to connect multiple memory layers to logic chips, offering high bandwidth (up to several TB/s) and lower power consumption compared to traditional DRAM, but with higher costs and complexity [4][6]. Group 2: Shift in Market Demand - The focus in AI development has shifted from computational power to real-time inference capabilities, driven by applications such as voice assistants, translation, customer service, and autonomous systems, where high latency is a critical concern [6]. - Nvidia's acquisition of Groq's technology is not just about enhancing AI accelerator capabilities but is fundamentally linked to SRAM's strengths in providing extremely low-latency memory access, which is essential for real-time AI applications [5][6].