  Extreme edge platforms, such as in-vehicle smart devices, require efficient
deployment of quantized deep neural networks (DNNs) to enable intelligent
applications with limited amounts of energy, memory, and computing resources.
However, many edge devices struggle to boost inference throughput of various
quantized DNNs due to the varying quantization levels, and these devices lack
floating-point (FP) support for on-device learning, which prevents them from
improving model accuracy while ensuring data privacy. To tackle the challenges
above, we propose a precision-scalable RISC-V DNN processor with on-device
learning capability. It facilitates diverse precision levels of fixed-point DNN
inference, spanning from 2-bit to 16-bit, and enhances on-device learning
through improved support with FP16 operations. Moreover, we employ multiple
methods such as FP16 multiplier reuse and multi-precision integer multiplier
reuse, along with balanced mapping of FPGA resources, to significantly improve
hardware resource utilization. Experimental results on the Xilinx ZCU102 FPGA
show that our processor significantly improves inference throughput by
1.6$\sim$14.6$\times$ and energy efficiency by 1.1$\sim$14.6$\times$ across
various DNNs, compared to the prior art, XpulpNN. Additionally, our processor
achieves a 16.5$\times$ higher FP throughput for on-device learning.
