CNNs (convolutional neural networks) have been recently successfully applied for a wide range of cognitive challenges. Given high computational demands of CNNs, custom hardware accelerators are vital for boosting their performance. The high energy-efficiency, computing capabilities and reconfigurability of FPGA make it a promising platform for hardware acceleration of computation-intensive CNNs. Especially, the higher energy efficiency of FPGAs than GPUs make them attractive.
Our paper surveys 75+ techniques for implementing and optimizing CNN algorithms on FPGA. Accepted in Neural Computing and Applications journal 2018.
Available here.
Our paper surveys 75+ techniques for implementing and optimizing CNN algorithms on FPGA. Accepted in Neural Computing and Applications journal 2018.
Available here.