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High Performance Simulation of Spiking Neural Network on GPGPUs

机译:GPGPU上尖刺神经网络的高性能模拟

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Spiking neural network (SNN) is the most commonly used computational model for neuroscience and neuromorphic computing communities. It provides more biological reality and possesses the potential to achieve high computational power and energy efficiency. Because existing SNN simulation frameworks on general-purpose graphics processing units (GPGPUs) do not fully consider the biological oriented properties of SNNs, like spike-driven, activity sparsity, etc., they suffer from insufficient parallelism exploration, irregular memory access, and load imbalance. In this article, we propose specific optimization methods to speed up the SNN simulation on GPGPU. First, we propose a fine-grained network representation as a flexible and compact intermediate representation (IR) for SNNs. Second, we propose the cross-population/-projection parallelism exploration to make full use of GPGPU resources. Third, sparsity aware load balance is proposed to deal with the activity sparsity. Finally, we further provide dedicated optimization to support multiple GPGPUs. Accordingly, BSim, a code generation framework for high-performance simulation of SNN on GPGPUs is also proposed. Tests show that, compared to a state-of-the-art GPU-based SNN simulator GeNN, BSim achieves 1.41x similar to 9.33x speedup for SNNs with different configurations; it outperforms other simulators much more.
机译:尖刺神经网络(SNN)是最常用的神经科学和神经形态计算社区的计算模型。它提供了更多的生物现实,并具有实现高计算能力和能源效率的潜力。由于通用图形处理单元(GPGPU)上的现有SNN仿真框架没有完全考虑SNN的生物学取向特性,如尖峰驱动,活动稀疏等,它们遭受不足的并行探索,不规则内存访问和负载不平衡。在本文中,我们提出了具体的优化方法,以加快GPGPU上的SNN仿真。首先,我们提出了一种细粒度的网络表示,作为SNNS的灵活和紧凑的中间表示(IR)。其次,我们提出了跨人口/ - 分解并行探索,充分利用GPGPU资源。第三,提出了稀疏意识的负载余额来处理活动稀疏性。最后,我们进一步提供了专用优化来支持多个GPGPU。因此,还提出了BSIM,用于GPGPU上的SNN高性能模拟的代码生成框架。测试表明,与最先进的基于GPU的SNN模拟器Genn相比,BSIM实现了1.41倍,类似于具有不同配置的SNN的9.33倍的加速;它比其他模拟器更加优于更多。

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    Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China|Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China;

    Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China|Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China;

    Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China|Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China;

    Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China|Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Spiking neural network; SNN simulation; GPGPU; load balance; computational neuroscience;

    机译:尖峰神经网络;SNN仿真;GPGPU;负载平衡;计算神经科学;

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