3 accepted papers
Automatic speech recognition (ASR) is very important for mobile devices. However, deep neural network-based ASR demands a large number of computations, while the memory bandwidth and battery capacity of mobile devices are limited. Server-based implementations are mostly employed, but this increases…
Real-time automatic speech recognition (ASR) on mobile and embedded devices has been of great interests for many years. We present real-time speech recognition on smartphones or embedded systems by employing recurrent neural network (RNN) based acoustic models, RNN based language models, and beam-s…
Deep neural networks (DNNs) demand a very large amount of computation and weight storage, and thus efficient implementation using special purpose hardware is highly desired. In this work, we have developed an FPGA based fixed-point DNN system using only on-chip memory not to access external DRAM. Th…