STMicroelectronics says edge AI growth hinges on in-memory computing shift

• STMicroelectronics management flagged edge AI as shifting from cloud training costs to inference economics, pushing compute closer to data sources.
• Strategy centers on in-memory computing to cut energy from data movement, moving from near-memory designs toward SRAM and non-volatile in-memory architectures.
• Highlighted an 18 nm FD-SOI digital in-memory accelerator shown at ISSCC 2023, delivering 40 to 310 TOPS/W at up to 4-bit precision.
• Pointed to Neural-ART NPUs as the commercialization path, including STM32N6 as the first STM32 with built-in hardware AI acceleration.
• Emphasized toolchain readiness as key to adoption, citing compiler and quantization support within ST Edge AI tools and the STM32 AI ecosystem.

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