Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents methodology of feedback-controlled dynamic approximation to enable energy-accuracy trade-off in digital recurrent neural network (RNN). A low-por digital RNN engine is presented that employs the proposed dynamic approximation. The on-chip feedback controller is realized by utilizing hysteretic or proportional controller. The dynamic adaptation of bit-precisions during the RNN computation is selected as approximation approach. Considering various applications, the digital RNN engine designed in 28nm CMOS shows ~36% average energy saving compared to the baseline case, with only ~4% of accuracy degradation on average.
['Jaeha Kung', 'Duckhwan Kim', 'Saibal Mukhopadhyay']
I am writing a research paper on a idea Dynamic Approximation with Feedback Control for Energy-Efficient Recurrent Neural Network Hardware
[{"from": "human", "value": "I am writing a research paper on a idea Dynamic Approximation with Feedback Control for Energy-Efficient Recurrent Neural Network Hardware"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents methodology of feedback-controlled dynamic approximation to enable energy-accuracy trade-off in digital recurrent neural network (RNN). A low-por digital RNN engine is presented that employs the proposed dynamic approximation. The on-chip feedback controller is realized by utilizing hysteretic or proportional controller. The dynamic adaptation of bit-precisions during the RNN computation is selected as approximation approach. Considering various applications, the digital RNN engine designed in 28nm CMOS shows ~36% average energy saving compared to the baseline case, with only ~4% of accuracy degradation on average."}]