存算一体进入「产业化时刻」，谁在领跑规模化交付？

TL;DR - Compute-in-memory is moving from experimental prototypes to mass-produced AI chips as edge models intensify bandwidth and power constraints. Commercial success increasingly depends on manufacturing yield, software tooling, and application fit—not peak performance alone.

• CIM approaches vary by memory medium (SRAM, DRAM, Flash, RRAM, or MRAM), computation type (digital, analog, or hybrid), and 2D versus 3D integration.
• Commercial deployments include low-power Flash-based chips in wearables and Houmo’s digital SRAM-based M50, which targets local large-model inference in PCs, robots, and edge servers.
• Industrialization requires reproducible performance across process, voltage, and temperature variations, plus software stacks that map, compile, and quantize models effectively.
• Mature manufacturing and clear use cases currently favor Flash and SRAM designs, while emerging RRAM and MRAM approaches still face consistency and reliability challenges.