Tiny machine learning (TinyML), executing AI workloads on resource and power
strictly restricted systems, is an important and challenging topic. This brief
firstly presents an extremely tiny backbone to construct high efficiency CNN
models for various visual tasks. Then, a specially designed neural co-processor
(NCP) is interconnected with MCU to build an ultra-low power TinyML system,
which stores all features and weights on chip and completely removes both of
latency and power consumption in off-chip memory access. Furthermore, an
application specific instruction-set is further presented for realizing agile
development and rapid deployment. Extensive experiments demonstrate that the
proposed TinyML system based on our model, NCP and instruction set yields
considerable accuracy and achieves a record ultra-low power of 160mW while
implementing object detection and recognition at 30FPS. The demo video is
available on https://www.youtube.com/watch?v=mIZPxtJ-9EY.

通过在芯片上存储全部功能和权重，完全消除了离线内存访问的延迟和能耗，并提出了一种应用特定的指令集，以实现敏捷开发和快速部署。实验表明，基于该模型、NCP 和指令集的 TinyML 系统在实现物体检测和识别时，取得了可观的准确度，并实现了纪录级的超低功耗，只有 160mW。