Department of Machine Intelligence and Systems Engineering, Faculty of Systems Science and Technology, Akita Prefectural University, Yurihonjo, Japan

---

Abstract—This paper presents a digital hardware Back- Propagation (BP) model for real-time learning in the field of video image processing. The model is a layer parallel architecture with a 16-bit fixed point specialized for video image processing. We have compared our model with a standard BP model that used a double-precision floating point. Simulation results show that our model has equal capabilities to those of the standard BP model. We have implemented the model on an FPGA board that we originally designed and developed for experimental use as a platform for real-time video image processing. Experimental results show that our model performed 100,000 epochs/frame learning that corresponds to 90 MCUPS and was able to test all pixels on interlace video images.

Index Terms—Field programmable gate arrays, backpropagation neural networks, real-time video image processing, million connection updates per second.

[PDF]

Cite: H. Madokoro and K. Sato, " Hardware Implementation of Back-Propagation Neural Networks for Real-Time Video Image Learning and Processing," Journal of Computers vol. 8, no. 3, pp. 559-566, 2013.