  Cyber-Physical Systems (CPSs) have been pervasive including smart grid,
autonomous automobile systems, medical monitoring, process control systems,
robotics systems, and automatic pilot avionics. As usually implemented on
embedded devices, CPS is typically constrained by computation capacity and
energy consumption. In some CPS applications such as telemedicine and advanced
driving assistance system (ADAS), data processing on the embedded devices is
preferred due to security/safety and real-time requirement. Therefore, high
efficiency is highly desirable for such CPS applications. In this paper we
present CeNN quantization for high-efficient processing for CPS applications,
particularly telemedicine and ADAS applications. We systematically put forward
powers-of-two based incremental quantization of CeNNs for efficient hardware
implementation. The incremental quantization contains iterative procedures
including parameter partition, parameter quantization, and re-training. We
propose five different strategies including random strategy, pruning inspired
strategy, weighted pruning inspired strategy, nearest neighbor strategy, and
weighted nearest neighbor strategy. Experimental results show that our approach
can achieve a speedup up to 7.8x with no performance loss compared with the
state-of-the-art FPGA solutions for CeNNs.
