Due to the ability to implement customized topology, FPGA is increasingly used to deploy SNNs in both embedded and high-performance applications. In this paper, we survey state-of-the-art SNN implementations and their applications on FPGA. We collect the recent widely-used spiking neuron models, network structures, and signal encoding formats, followed by the enumeration of related hardware design schemes for FPGA-based SNN implementations. Compared with the previous surveys, this manuscript enumerates the application instances that applied the above-mentioned technical schemes in recent research. Based on that, we discuss the actual acceleration potential of implementing SNN on FPGA. According to our above discussion, the upcoming trends a...
Classical Neural Networks consume many resources when they are implemented directly in hardware; but...
Summarization: Neuromorphic computing is expanding by leaps and bounds through custom integrated cir...
International audienceMachine learning is yielding unprecedented interest in research and industry, ...
Due to the ability to implement customized topology, FPGA is increasingly used to deploy SNNs in bot...
Spiking Neural Networks (SNN) are an emerging type of biologically plausible and efficient Artificia...
The operation and structure of the human brain has inspired the development of next generation smart...
Compiler frameworks are crucial for the widespread use of FPGA-based deep learning accelerators. The...
Spiking Neural Networks (SNN) are an emerging type of biologically plausible and efficient Artificia...
Recently, researchers have shown an increased interest in more biologically realistic neural network...
The automatic design of intelligent systems has been inspired by biology, specifically the operation...
© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommo...
Field-programmable gate arrays (FPGAs) can provide an efficient programmable resource for implementi...
Deep Learning (DL) has contributed to the success of many applications in recent years. The applicat...
FPGA devices have witnessed popularity in their use for the rapid prototyping of biological Spiking ...
Spiking Neural Networks (SNN) is considered the third generation of neural networks. This type of ne...
Classical Neural Networks consume many resources when they are implemented directly in hardware; but...
Summarization: Neuromorphic computing is expanding by leaps and bounds through custom integrated cir...
International audienceMachine learning is yielding unprecedented interest in research and industry, ...
Due to the ability to implement customized topology, FPGA is increasingly used to deploy SNNs in bot...
Spiking Neural Networks (SNN) are an emerging type of biologically plausible and efficient Artificia...
The operation and structure of the human brain has inspired the development of next generation smart...
Compiler frameworks are crucial for the widespread use of FPGA-based deep learning accelerators. The...
Spiking Neural Networks (SNN) are an emerging type of biologically plausible and efficient Artificia...
Recently, researchers have shown an increased interest in more biologically realistic neural network...
The automatic design of intelligent systems has been inspired by biology, specifically the operation...
© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommo...
Field-programmable gate arrays (FPGAs) can provide an efficient programmable resource for implementi...
Deep Learning (DL) has contributed to the success of many applications in recent years. The applicat...
FPGA devices have witnessed popularity in their use for the rapid prototyping of biological Spiking ...
Spiking Neural Networks (SNN) is considered the third generation of neural networks. This type of ne...
Classical Neural Networks consume many resources when they are implemented directly in hardware; but...
Summarization: Neuromorphic computing is expanding by leaps and bounds through custom integrated cir...
International audienceMachine learning is yielding unprecedented interest in research and industry, ...