iRNN: Integer-only Recurrent Neural Network

• URL: http://arxiv.org/abs/2109.09828v1
• Date: Mon, 20 Sep 2021 20:17:40 GMT
• Title: iRNN: Integer-only Recurrent Neural Network
• Authors: Eyy\"ub Sari, Vanessa Courville, Vahid Partovi Nia
• Abstract summary: We present a quantization-aware training method for obtaining a highly accurate integer-only recurrent neural network (iRNN) Our iRNN maintains similar performance as its full-precision counterpart, their deployment on smartphones improves the runtime performance by $2times$, and reduces the model size by $4times$.
• Score: 0.8766022970635899
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recurrent neural networks (RNN) are used in many real-world text and speech applications. They include complex modules such as recurrence, exponential-based activation, gate interaction, unfoldable normalization, bi-directional dependence, and attention. The interaction between these elements prevents running them on integer-only operations without a significant performance drop. Deploying RNNs that include layer normalization and attention on integer-only arithmetic is still an open problem. We present a quantization-aware training method for obtaining a highly accurate integer-only recurrent neural network (iRNN). Our approach supports layer normalization, attention, and an adaptive piecewise linear approximation of activations, to serve a wide range of RNNs on various applications. The proposed method is proven to work on RNN-based language models and automatic speech recognition. Our iRNN maintains similar performance as its full-precision counterpart, their deployment on smartphones improves the runtime performance by $2\times$, and reduces the model size by $4\times$.

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