东北大学与丰田供应商爱信在 TSMC 16nm 工艺打造了一颗 AI SoC,片上集成 32MB 嵌入式 STT-MRAM,同时存放精简 Linux 启动镜像和 Tiny YOLOv3 物体检测模型权重,启动与推理无需外置 DRAM 或 SSD。启动耗时 65.7 毫秒(快 39 倍),物体检测功耗约 50.7 mJ/帧,采用权重共享和 8/16-bit 混合运算来压缩模型。
AI 生成摘要 · 以原文为准
Tohoku University and Toyota-supplier Aisin built a 16 nm AI chip (SoC) with 32 MB of embedded STT-MRAM that holds both a compact Linux boot image and Tiny YOLOv3 (object detection AI model) weights on-chip.
No external DRAM or SSD is needed for startup or inference. It boots in 65.7 milliseconds (39× faster) and runs object detection at ~50.7 mJ/frame with big energy savings. They used weight sharing and hybrid 8/16-bit math to fit the model.
The chip also includes on-chip SRAM (used for feature maps and other working data) and a DDR interface, so external memory can still be attached when larger models or more capacity are needed. TSMC manufactures the chip.
Nice real-silicon demo of near-memory computing for edge AI — useful for automotive cameras, sensor fusion, and always-ready devices that need instant-on and low standby power. Not revolutionary: Others – Hailo, Ambarella, Renesas, etc. – already use large on-chip SRAM, embedded MRAM, or in-package DRAM, but it’s a good example of the gains when OS + model live in non-volatile on-chip memory. $TSM $AMBA #Hailo #Renesas #semiconductors
来源:Dan Nystedt · x.com