@article {1435525,
	title = {A 25mm2 SoC for IoT Devices with 18ms Noise Robust Speech-to-Text Latency via Bayesian Speech Denoising and Attention-Based Sequence-to-Sequence DNN Speech Recognition in 16nm FinFET},
	journal = {International Solid-State Circuits Conference (ISSCC{\textquoteright}21)},
	year = {2021},
	abstract = {Automatic speech recognition (ASR) using deep learning is essential for user interfaces on IoT devices. However, previously published ASR chips [4-7] do not consider realistic operating conditions, which are typically noisy and may include more than one speaker. Furthermore, several of these works have implemented only small-vocabulary tasks, such as keyword-spotting (KWS), where context-blind deep neural network (DNN) algorithms are adequate. However, for large-vocabulary tasks (e.g., \&gt;100k words), the more complex bidirectional RNNs with an attention mechanism [1] provide context learning in long sequences, which improve ASR accuracy by up to 62\% on the 200kwords LibriSpeech dataset, compared to a simpler unidirectional RNN (Fig. 9.8.1). Attention-based networks emphasize the most relevant parts of the source sequence during each decoding time step. In doing so, the encoder sequence is treated as a soft-addressable memory whose positions are weighted based on the state of the decoder RNN. Bidirectional RNNs learn past and future temporal information by concatenating forward and backward time steps.},
	url = {https://doi.org/10.1109/ISSCC42613.2021.9366062},
	author = {Tambe, Thierry and Yang, En-Yu and Glenn G. Ko and Chai, Yuji and Coleman Hooper and Marco Donato and Paul N. Whatmough and Alexander M. Rush and David Brooks and Gu-Yeon Wei}
}
