Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The efficiency and the accuracy of a digital feedforward neural networks must be optimized to obtain both high classification rate and minimum area on chip.  an efficient MLP digital implementation. The key features of the hardware implementation are the virtual neuron based architecture and the use of the sinusoidal activation function for the hidden layer. The effectiveness of the proposed solutions has been evaluated developing different FPGA based neural prototypes for the high energy physics domain and the automatic road sign recognition domain. The use of the sinusoidal activation function decreases hardware resource employment of about 32% when compared with the standard sigmoid based neuron implementation. The virtual neuron implementation makes efficient the mapping of a neural network into hardware devices since it leads to a significant decreasing of concurrent memory access.
['Salvatore Vitabile', 'Vincenzo Conti', 'Fulvio Gennaro', 'Filippo Sorbello']
I am writing a research paper on a idea Efficient MLP digital implementation on FPGA
[{"from": "human", "value": "I am writing a research paper on a idea Efficient MLP digital implementation on FPGA"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The efficiency and the accuracy of a digital feedforward neural networks must be optimized to obtain both high classification rate and minimum area on chip.  an efficient MLP digital implementation. The key features of the hardware implementation are the virtual neuron based architecture and the use of the sinusoidal activation function for the hidden layer. The effectiveness of the proposed solutions has been evaluated developing different FPGA based neural prototypes for the high energy physics domain and the automatic road sign recognition domain. The use of the sinusoidal activation function decreases hardware resource employment of about 32% when compared with the standard sigmoid based neuron implementation. The virtual neuron implementation makes efficient the mapping of a neural network into hardware devices since it leads to a significant decreasing of concurrent memory access."}]