# Approximate LSTM Computing for Energy-Efficient Speech Recognition
## 1 Introduction  
In the last few years, speech recognition using deep neural networks (DNNs) has become an important challenge in the internet-of-things (IoT) communities as well as the artificial intelligence (AI) industries [1]. In general, to handle sequential data such as voice signals, it is well known that the recurrent neural network (RNN) is suitable to improve the recognition accuracy by activating the prior histories during the inference processing [2]. In order to achieve the attractive algorithm-level performance that can be even applied to the commercialized products; however, the contemporary RNN-based algorithm normally necessitates a huge amount of computing resources [3]. For example, the recent long short-term memory (LSTM) model utilizes additional gate operations to provide state-of-the-art end-to-end speech recognition; however, it significantly increased the memory overheads storing the excessive number of trained parameters [4]. Considering the amount of accessing energy for external DRAMs [5], therefore, the modern DNN-based speech recognition solutions are mainly available at the server-scale or cloud-level computing platforms. Targeting the on-device speech recognition, on the other hand, it is impractical to directly apply the recent LSTM architectures at the resource-limited IoT or mobile platforms. Several studies have presented optimization techniques for reducing energy consumption of neural network processing [6; 7]. For example, the work from [8] provides the energy-accuracy trade-off by utilizingthe control gate, which is suitable for handling the image classification problem. The pruning-based approach can reduce the number of multiply-accumulate (MAC) operations as well as the number of memory accesses by eliminating less important parameters [9]. Considering the continuous speech signal, the conventional LSTM-based network is modified in [10] by adding more gate networks that determine the activation of state updates. As the computing steps in LSTM operations can be expressed as matrix-to-matrix multiplications, the approximate computing from the singular-value decomposition (SVD) is applied to relax the computational complexity without degrading the algorithm-level accuracy [11]. It is also possible to further reduce the computing costs of each RNN/LSTM cell by adopting the concept of delta-network, which adjusts the level of approximate parts by changing the sparsity level dynamically [12]. More aggressive method adopting binary neural network (BNN) can be applied to minimize the energy consumption of DNN processing [13]. However, it is generally reported that such a BNN-based method severely degrades the recognition performance; even its flexible hardware can be easily implemented by utilizing the latch-based scratch-pad memories (SCMs). In this work, the proposed work reduces computational complexity through an approximate computing approach that focuses on complicated networks utilizing multiple bits to represent weight values, which normally provide more accurate accuracy than BNN models. In addition to the previous works, there are still potential rooms to enhance the energy-efficiency of DNN-based speech recognition if we carefully tack account of the properties of input speech signals. Compared to the practical time interval to construct each input state of LSTM (or RNN) structure, more precisely, the input states from the speech data change quite slowly compared with other types of inputs [12]. Therefore, it is reasonable to assume that the adjacent input states tend to be highly similar to each other. Starting from our prior work in [14], this paper presents an energy-efficient LSTM accelerator design that performs the approximate LSTM operations based on the similarity-based cell activation. As described in [14], we first introduce the similarity score measuring how two adjacent LSTM inputs are similar to each other.