On the other hand, FPGA is a promising hardware platform for accelerating deep neural networks (DNNs) thanks to its re-programmability and power efficiency. In this chapter, we review essential computations in
These ASIC SNN accelerators have good performance, but they do not provide enough flexibility to accommodate the rapid development of SNN models, let along the high cost. The FPGA today has rich on-chip hardware resources and the reconfigurable architecture, which is able to meet the different ...
More specifically, several deep CNN accelerators have been planned on FPGA-based platform, due to its fast development round, reconfigure-ability, and high performance. The FPGA is extremely faster than the CPU because it based on parallel mechanism, as well as, consumes very low energy. This ...
various accelerators for deep CNN have been proposed based on FPGA platform because it has advantages of high performance, reconfigurability, and fast development round, etc. Although current FPGA accelerators have demonstrated better performance over generic processors, th...
Recently, rapid growth of modern applications based on deep learning algorithms has further improved research and implementations. Especially, various accelerators for deep CNN have been proposed based on FPGA platform because it has advantages of high performance, reconfigurability,...
Guan, Y., et al.: FP-DNN: An automated framework for mapping deep neural networks onto FPGAs with RTL-HLS hybrid templates. In: FCCM Symposium (2017) Google Scholar Guo, K., et al.: [DL] A survey of FPGA-based neural network inference accelerators. ACM TRETS 12(1), 1–26 (2019...
英文引用格式：SHI Y Q，JING N F.Evaluation method based on FPGA emulation for resistive neural network accelerators［J］. Computer Engineering，2021，47（12）：209-214.Evaluation Method Based on FPGA Emulation for Resistive Neural Network Accelerators SHI Yongquan，JING Naifeng （School of Electronic...
Li, “DeepBurning: Automatic Generation of FPGA-based Learning Accelerators for the Neural Network ...
In these years, most of neural network accelerators are implemented on FPGA for high throughout, low power consumption and movable portability [9]. For real-time applications like target detection, speech recognition and video processing, various FPGA based neural network accelerators have been ...
Guo K, Zeng S, Yu J, Wang Y, Yang H (2019) [dl] a survey of fpga-based neural network inference accelerators. ACM Trans. Reconfigurable Technol. Syst 12(1) Mittal S (2020) A survey of fpga-based accelerators for convolutional neural networks. Neural Comput Applic 32(4):1109–1139 Ar...