Ensemble Machine Learning (EML) consists of the combination of multiple Artificial Intelligence algorithms. This paper presents an efficient FPGA implementation of an Ensemble based on Long Short-Term Memory Networks (LSTM). For an efficient implementation, the proposed design uses the Partial Reconfiguration function available for FPGAs. Results are presented in terms of resources utilization, reconfiguration speed, power consumption and maximum clock frequency.
Cardarilli, G.c., Di Nunzio, L., Fazzolari, R., Giardino, D., Matta, M., Re, M., et al. (2019). Efficient ensemble machine learning implementation on FPGA using partial reconfiguration. In ApplePies 2018: Applications in electronics pervading industry, environment and society (pp.253-259). Springer [10.1007/978-3-030-11973-7_29].
Efficient ensemble machine learning implementation on FPGA using partial reconfiguration
Cardarilli G. C.;Di Nunzio L.;Fazzolari R.;Re M.;Silvestri F.;Spano S.
2019-01-01
Abstract
Ensemble Machine Learning (EML) consists of the combination of multiple Artificial Intelligence algorithms. This paper presents an efficient FPGA implementation of an Ensemble based on Long Short-Term Memory Networks (LSTM). For an efficient implementation, the proposed design uses the Partial Reconfiguration function available for FPGAs. Results are presented in terms of resources utilization, reconfiguration speed, power consumption and maximum clock frequency.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
