在数字神经形态架构上优化基于事件的神经网络:一个全面的设计空间探索
Yingfu Xu1, Kevin Shidqi1, Gert-Jan van Schaik1
1IMEC, Eindhoven, Netherlands.
Frontiers in neuroscience
|April 12, 2024
概括
神经形态处理器可以实现更好的能源效率和更低的AI任务延迟. 这项研究引入了尖峰分组和事件驱动的深度第一卷积,以优化神经网络推断在神经形态硬件上.
科学领域:
- 神经形态计算是一种神经形态计算.
- 人工智能硬件人工智能硬件
- 深度学习加速器深度学习加速器
背景情况:
- 神经形态处理器提供了低延迟和节能AI处理的潜力,使用大脑启发的设计.
- 当前的神经形态解决方案在与传统深度学习加速器的性能和效率相匹配方面面临挑战.
- 关键的挑战包括减少事件驱动的处理开销和提高近内存计算映射效率.
研究的目的:
- 为了解决当前神经形态处理器的性能和面积效率限制.
- 在SENECA神经形态架构上优化基于事件的神经网络推断.
- 提出新的技术来提高能源效率,延迟和区域效率.
主要方法:
- 对于神经形态建筑的全面设计空间探索.
- 开发尖峰分组技术,以减少事件驱动处理中的能量和延迟.
- 引入事件驱动的深度第一卷积,以提高神经形态处理器上的CNN的面积效率和延迟.
主要成果:
- 与最先进的神经形态处理器相比,优化实现了6×300×的能源效率提高.
- 在各种任务中,延迟提高了3×15×,区域效率提高了3×100×.
- 在关键字发现,传感器融合,数字识别和高分辨率物体检测方面表现出有效性.
结论:
- 建议的优化显著提高了基于事件的神经网络推断在神经形态硬件上的性能和效率.
- 尖端分组和事件驱动的深度第一卷积是克服当前神经形态处理挑战的有效策略.
- 这些优化显示了在广泛的基于事件的神经形态处理器中概括的潜力.
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