EPSim-C: A Parallel Epoch-Based Cycle-Accurate Microarchitecture Simulator Using Cloud Computing

Kim, Minseong; Kim, Seon Wook; Han, Youngsun

doi:10.3390/electronics8060716

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…To reduce this difficulty, virtual platforms are presented as a perfect solution for evaluating whole systems and analyzing hardware/software proposals before their implementation stage. These virtual platforms use hardware/software co-simulation techniques profusely described in the literature [2][3][4]. In those, the software and hardware designs are combined at different levels of abstraction and with different timing accuracy.…”

Section: Introductionmentioning

confidence: 99%

Enabling Parallelized-QEMU for Hardware/Software Co-Simulation Virtual Platforms

et al. 2021

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Presently, the trend is to increase the number of cores per chip. This growth is appreciated in Multi-Processor System-On-Chips (MPSoC), composed of more cores in heterogeneous and homogeneous architectures in recent years. Thus, the difficulty of verification of this type of system has been great. The hardware/software co-simulation Virtual Platforms (VP) are presented as a perfect solution to address this complexity, allowing verification by simulation/emulation of software and hardware in the same environment. Some works parallelized the software emulator to reduce the verification times. An example of this parallelization is the QEMU (Quick EMUlator) tool. However, there is no solution to synchronize QEMU with the hardware simulator in this new parallel mode. This work analyzes the current software emulators and presents a new method to allow an external synchronization of QEMU in its parallelized mode. Timing details of the cores are taken into account. In addition, performance analysis of the software emulator with the new synchronization mechanism is presented, using: (1) a boot Linux for MPSoC Zynq−7000 (dual-core ARM Cortex-A9) (Xilinx, San Jose, CA, USA); (2) an FPGA-Linux co-simulation of a power grid monitoring system that is subsequently implemented in an industrial application. The results show that the novel synchronization mechanism does not add any appreciable computational load and enables parallelized-QEMU in hardware/software co-simulation virtual platforms.

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Section: Introductionmentioning

confidence: 99%

Enabling Parallelized-QEMU for Hardware/Software Co-Simulation Virtual Platforms

et al. 2021

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Parallel Recurrent Convolutional Neural Networks-Based Music Genre Classification Method for Mobile Devices

et al. 2020

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With the rapid development of the mobile internet of things (IoTs) and mobile sensing devices, a large amount of mobile computing-oriented applications have attracted attention both from industry and academia. Deep learning based methods have achieved great success in artificial intelligence (AI) oriented applications. To advance the development of AI-based IoT systems, effective and efficient algorithms are in urgent need for IoT Edge Computing. Time-series data classification is an ongoing problem in applications for mobile devices (e.g. music genre classification on mobile phones). However, the traditional methods require field expertise to extract handcrafted features from the time-series data. Deep learning has been demonstrated to be effective and efficient in this kind of data. Nevertheless, the existing works neglect some of the sequential relationships found in the time-series data, which are significant for time-series data classification. Considering the aforementioned limitations, we propose a hybrid architecture, named the parallel recurrent convolutional neural network (PRCNN). The PRCNN is an end-to-end training network that combines feature extraction and time-series data classification in one stage. The parallel CNN and Bi-RNN blocks focus on extracting the spatial features and temporal frame orders, respectively, and the outputs of two blocks are fused into one powerful representation of the time-series data. Then, the syncretic vector is fed into the softmax function for classification. The parallel network structure guarantees that the extracted features are robust enough to represent the time-series data. Moreover, the experimental results demonstrate that our proposed architecture outperforms the previous approaches applied to the same datasets. We also take the music data as an example to conduct contrastive experiments to verify that our additional parallel Bi-RNN block can improve the performance of time-series classification compared with utilizing CNNs alone. INDEX TERMS Convolutional neural networks, parallel, time-series data classification.

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EPSim-C: A Parallel Epoch-Based Cycle-Accurate Microarchitecture Simulator Using Cloud Computing

Cited by 2 publications

References 48 publications

Enabling Parallelized-QEMU for Hardware/Software Co-Simulation Virtual Platforms

Enabling Parallelized-QEMU for Hardware/Software Co-Simulation Virtual Platforms

Parallel Recurrent Convolutional Neural Networks-Based Music Genre Classification Method for Mobile Devices

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