Qinlu He^1,*, Fan Zhang¹, Genqing Bian¹, Weiqi Zhang¹, Zhen Li²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2833-2850, 2024, DOI:10.32604/cmc.2024.046807

Abstract Spark, a distributed computing platform, has rapidly developed in the field of big data. Its in-memory computing feature reduces disk read overhead and shortens data processing time, making it have broad application prospects in large-scale computing applications such as machine learning and image processing. However, the performance of the Spark platform still needs to be improved. When a large number of tasks are processed simultaneously, Spark’s cache replacement mechanism cannot identify high-value data partitions, resulting in memory resources not being fully utilized and affecting the performance of the Spark platform. To address the problem that… More >

Hyun-Wook Son¹, Ali A. Al-Hamid^1,2, Yong-Seok Na¹, Dong-Yeong Lee¹, Hyung-Won Kim^1,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1665-1687, 2023, DOI:10.32604/cmc.2023.038760

Abstract This paper presents the architecture of a Convolution Neural Network (CNN) accelerator based on a new processing element (PE) array called a diagonal cyclic array (DCA). As demonstrated, it can significantly reduce the burden of repeated memory accesses for feature data and weight parameters of the CNN models, which maximizes the data reuse rate and improve the computation speed. Furthermore, an integrated computation architecture has been implemented for the activation function, max-pooling, and activation function after convolution calculation, reducing the hardware resource. To evaluate the effectiveness of the proposed architecture, a CNN accelerator has been… More >