基于CNN的手写识别芯片关键计算模块硬件加速方案设计

Handwritten recognition, as a core part of input method, has been the focus of scientific and technological research in recent years for its rapidity and accuracy. In recent decades, with the efforts of many scholars and researchers, handwriting recognition technology has made great progress. However, due to the constraints of CPU, memory and other performance of mobile devices, handwriting recognition still has some problems, such as slow speed and low recognition accuracy. This delay will lead to large power consumption, shorten the life of devices, and result in poor user experience. Under the guidance of CNN theory, hardware acceleration of handwriting recognition application is an effective way to solve such problems. By analyzing the parallelism of Lenet-5 structure, we use the advantages of good scalability and high communication bandwidth of multicast network to mount hardware acceleration units on the nodes of network-on-chip, coordinate the operation of each hardware acceleration unit to complete the hardware acceleration of the whole identification process.