Yixin Ding¹, Xinjian Zhao¹, Zicheng Wu¹, Yichen Zhu², Longkun Bai², Hao Han^2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5977-5993, 2025, DOI:10.32604/cmc.2025.065672 - 30 July 2025

Abstract Fuzz testing is a widely adopted technique for uncovering bugs and security vulnerabilities in embedded firmware. However, many embedded systems heavily rely on peripherals, rendering conventional fuzzing techniques ineffective. When peripheral responses are missing or incorrect, fuzzing a firmware may crash or exit prematurely, significantly limiting code coverage. While prior re-hosting approaches have made progress in simulating Memory-Mapped Input/Output (MMIO) and interrupt-based peripherals, they either ignore Direct Memory Access (DMA) or handle it oversimplified. In this work, we present ADFEmu, a novel automated firmware re-hosting framework that enables effective fuzzing of DMA-enabled firmware. ADFEmu integrates… 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 - 30 August 2023

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 >

Jeongha Lee¹, Hyokyung Bahn^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 607-629, 2023, DOI:10.32604/cmc.2023.039236 - 08 June 2023

Abstract Due to the recent trend of software intelligence in the Fourth Industrial Revolution, deep learning has become a mainstream workload for modern computer systems. Since the data size of deep learning increasingly grows, managing the limited memory capacity efficiently for deep learning workloads becomes important. In this paper, we analyze memory accesses in deep learning workloads and find out some unique characteristics differentiated from traditional workloads. First, when comparing instruction and data accesses, data access accounts for 96%–99% of total memory accesses in deep learning workloads, which is quite different from traditional workloads. Second, when… More >