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In this paper, a neural network based real-time speech recognition (SR)system is developed using an FPGA for very low-power operation. The implementedsystem employs two recurrent neural networks (RNNs); one is aspeech-to-character RNN for acoustic modeling (AM) and the other is forcharacter-level language modeling (LM). The system also employs a statisticalword-level LM to improve the recognition accuracy. The results of the AM, thecharacter-level LM, and the word-level LM are combined using a fairly simpleN-best search algorithm instead of the hidden Markov model (HMM) based network.The RNNs are implemented using massively parallel processing elements (PEs) forlow latency and high throughput. The weights are quantized to 6 bits to storeall of them in the on-chip memory of an FPGA. The proposed algorithm isimplemented on a Xilinx XC7Z045, and the system can operate much faster thanreal-time.

FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks