Due to the high throughput and high computing capability of convolutional neural networks (CNNs), researchers are paying increasing attention to the design of CNNs hardware accelerator architecture. Accordingly, in this paper, we propose a block parallel computing algorithm based on the matrix transformation computing algorithm (MTCA) to realize the convolution expansion and resolve the block problem of the intermediate matrix. It enables high parallel implementation on hardware. Moreover, we also provide a specific calculation method for the optimal partition of matrix multiplication to optimize performance. In our evaluation, our proposed method saves more than 60% of hardware storage space compared with the im2col(image to column) approach. More specifically, in the case of large-scale convolutions, it saves nearly 82% of storage space. Under the accelerator architecture framework designed in this paper, we realize the performance of 26.7GFLOPS-33.4GFLOPS (depending on convolution type) on FPGA(Field Programmable Gate Array) by reducing bandwidth and improving data reusability. It is 1.2×–4.0× faster than memory-efficient convolution (MEC) and im2col, respectively, and represents an effective solution for a large-scale convolution accelerator.

Convolutional Neural Networks (CNNs) are widely used in many fields. Due to their high throughput and high level of computing characteristics, however, an increasing number of researchers are focusing on how to improve the computational efficiency, hardware utilization, or flexibility of CNN hardware accelerators. Accordingly, this paper proposes a dynamically reconfigurable accelerator architecture that implements a Sparse-Winograd F(2 2.3 3)-based high-parallelism hardware architecture. This approach not only eliminates the pre-calculation complexity associated with the Winograd algorithm, thereby reducing the difficulty of hardware implementation, but also greatly improves the flexibility of the hardware; as a result, the accelerator can realize the calculation of Conventional Convolution, Grouped Convolution (GCONV) or Depthwise Separable Convolution (DSC) using the same hardware architecture. Our experimental results show that the accelerator achieves a 3x–4.14x speedup compared with the designs that do not use the acceleration algorithm on VGG-16 and MobileNet V1. Moreover, compared with previous designs using the traditional Winograd algorithm, the accelerator design achieves 1.4x–1.8x speedup. At the same time, the efficiency of the multiplier improves by up to 142%.

Late fusion multi-view clustering (LFMVC) algorithms aim to integrate the base partition of each single view into a consensus partition. Base partitions can be obtained by performing kernel k-means clustering on all views. This type of method is not only computationally efficient, but also more accurate than multiple kernel k-means, and is thus widely used in the multi-view clustering context. LFMVC improves computational efficiency to the extent that the computational complexity of each iteration is reduced from O(n3) to O(n) (where n is the number of samples). However, LFMVC also limits the search space of the optimal solution, meaning that the clustering results obtained are not ideal. Accordingly, in order to obtain more information from each base partition and thus improve the clustering performance, we propose a new late fusion multi-view clustering algorithm with a computational complexity of O(n2). Experiments on several commonly used datasets demonstrate that the proposed algorithm can reach quickly convergence. Moreover, compared with other late fusion algorithms with computational complexity of O(n), the actual time consumption of the proposed algorithm does not significantly increase. At the same time, comparisons with several other state-of-the-art algorithms reveal that the proposed algorithm also obtains the best clustering performance.