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Recently, the combination of convolutional neural network (CNN) and long short-term memory (LSTM) exhibits better performance than single network architecture. Most of these studies connect LSTM networks behind CNNs. When operating on hardware, the current design of CNN-LSTM is similar to a pipeline architecture. However, the classic structure lead to a feature loss when data is sent to LSTM since CNN is not good at extracting temporal features. At the same time, as the depth and scale increases, it will bring a huge amount of computation, which makes hardware implementation difficult. Based on that, a parallel CNN-LSTM architecture is proposed, in which two networks extract features from the input data synchronously, being proven to be more effective than classical CNN-LSTM. This paper designs a parallel CNN-LSTM computing device based on FPGA. The device is divided into control unit and operation unit. Control stream and data stream transport between the two units, ensuring the proper running of the device. A highly parallel multi-channel convolution layer and pooling layer are designed to improve the calculation efficiency. A 4-stage pipeline structure is adopted to implement the LSTM part. This paper makes full use of on-chip BRAM to design a look-up table for activation function approximation, reducing the resource consumption by 95% compared with the traditional polynomial approximation. Finally, we verify our device under cooperative spectrum sensing (CSS) and handwritten classification scenarios. Proposed device reaches higher accuracy in two scenarios compared with classic CNN-LSTM structure as well as faster calculating speed, and the overall project power is limited below 2W. The scalability and limitation of this computing device are also discussed.

This paper discusses the regularity of the GP-V’-rings in conjunction with idempotent reflexivity for the first time. We mainly discuss the weak and strong regularity of the GP-V’-rings using generalized weak ideals, weakly right ideals, and quasi-ideals. We show the following: (1) If R is an idempotent reflexive semi-abelian left GP-V’-ring whose every maximal essential left ideal is a generalized weak ideal, a weakly right ideal, or a quasi-ideal, then R is a reduced left weakly regular ring. (2) R is a strongly regular ring if and only if R is an idempotent reflexive semi-commutative left GP-V’-ring whose every maximal essential left ideal is a generalized weak ideal, a weakly right ideal, or a quasi-ideal. (3) If R is a semi-primitive idempotent reflexive ring whose every simple singular left R-module is flat, and every maximal left ideal is a generalized weak ideal, then, for any nonzero element a∈R, there exists a positive integer n such that an≠0, and RaR+lan=R.