在SpiNNaker 2上高效的SNN多核MAC阵列加速
Jiaxin Huang1, Florian Kelber2, Bernhard Vogginger2
1Infineon Technologies Dresden, Dresden, Germany.
Frontiers in neuroscience
|August 23, 2023
概括
本研究介绍了使用SpiNNaker 2的MAC数组进行神经网络 (SNN) 的平行加速算法. 这些新的算法显著减少了SNN推理的内存足迹和执行时间.
科学领域:
- 神经形态计算是一种神经形态计算.
- 人工智能的人工智能是人工智能.
- 计算机架构 计算机架构
背景情况:
- 尖端神经网络 (SNN) 提供低能耗计算,但在大型模型中面临时间挑战.
- 当前基于CPU的SNN处理对于广泛的数据集和复杂的架构来说是缓慢的.
- 有效的硬件加速对于实现SNN潜力至关重要.
研究的目的:
- 在SpiNNaker 2上引入SNN推理的并行加速算法.
- 调查MAC数组在处理元件 (PE) 中的集成,以提高SNN计算.
- 开发和评估用于空间时间负载平衡和性能优化的新算法.
主要方法:
- 将MAC阵列架构集成到SpiNNaker 2的处理元件中.
- 基于单核优化技术开发并行加速算法.
- 实施Echelon Reorder模型的信息密集算法.
- 调整多核二阶段分割和授权部署策略.
- 在各种SNN模型中进行基准测试,包括现实世界的应用和神经科学模型.
主要成果:
- 梯级优化算法在测试的SNN模型上实现了显著的内存足迹减少 (74.28%和85.78%).
- 执行时间大幅缩短,占序列ARM基线的≤24.56%.
- 证明了有效的时空负载平衡和优化性能.
- 该研究证实了稀疏矩阵-矩阵乘法 (SpGEMM) 优化对SNN的适用性.
结论:
- 拟议的并行算法和MAC数组集成为SNN推理提供了高效的加速.
- 介绍了针对SNN和MAC数组量身定制的新型SpGEMM优化算法.
- 这项工作将SpGEMM的应用扩展到SNNs,增强神经形态硬件性能.
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