Recently, deep learning with convolutional neural networks (CNNs) and recurrent neural networks (RNNs) has become universal in all-around applications. CNNs are used to support vision recognition and processing, and RNNs are able to recognize time varying entities and to support generative models. Also, combining both CNNs and RNNs can recognize time varying visual entities, such as action and gesture, and to support image captioning [1]. However, the computational requirements in CNNs are quite different from those of RNNs. Fig. 14.2.1 shows a computation and weight-size analysis of convolution layers (CLs), fully-connected layers (FCLs) and RNN-LSTM layers (RLs). While CLs require a massive amount of computation with a relatively small number of filter weights, FCLs and RLs require a relatively small amount of computation with a huge number of filter weights. Therefore, when FCLs and RLs are accelerated with SoCs specialized for CLs, they suffer from high memory transaction costs, low PE utilization, and a mismatch of the computational patterns. Conversely, when CLs are accelerated with FCL- and RL-dedicated SoCs, they cannot exploit reusability and achieve required throughput. So far, works have considered acceleration of CLs, such as [2-4], or FCLs and RLs like [5]. However, there has been no work on a combined CNN-RNN processor. In addition, a highly reconfigurable CNN-RNN processor with high energy-efficiency is desirable to support general-purpose deep neural networks (DNNs). 

  - CNN-RNN Support 

  - Dynamic Fixed-point with on-line Adaptation 

  - Quantization-table-based multiplier 

  - On-chip Stereo Matching

  - ISSCC 2017 [pdf]