一个22-pJ/spike 73-Mspikes/s 130k-compartment神经阵列收发器,具有基于导电性的突触和膜动态
Jongkil Park1,2,3, Sohmyung Ha2,4,5, Theodore Yu2,3
1Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea.
Frontiers in neuroscience
|September 13, 2023
概括
本研究介绍了集成和火阵列收发器 (IFAT) 芯片,这是一个新的神经形态计算系统. IFAT芯片有效模拟大规模的生物神经网络,具有高的生物物理现实性和灵活的连接性.
科学领域:
- 神经形态工程的神经形态工程
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 神经形态计算旨在在中复制生物神经系统.
- 现有的方法要么专注于详细的生物物理模拟,要么专注于抽象的生物细节以提高效率.
- 需要一种混合方法来实现大规模,高效和现实的神经网络仿真.
研究的目的:
- 开发一种神经形态系统,将生物物理现实主义与大规模实施效率相结合.
- 创建一个灵活的平台来模拟复杂的神经网络动态和连接.
- 为了实现高效的大规模模拟尖端神经网络和基于速率的激活.
主要方法:
- 集成和火阵列收发器 (IFAT) 芯片的开发.
- 模拟65,000个双隔间神经元与基于导电性的突触.
- 使用地址事件表示用于尖峰编码和可重新配置的突触连接.
- 实施双层微管道架构,以提高吞吐量和效率.
- 采用数字控制用于突触强度和单点数字偏移校准.
主要成果:
- IFAT芯片成功模拟了大规模的神经网络动态,具有高度的生物物理现实性.
- 实现了每秒7300万个尖峰的持续峰值吞吐量.
- 证明了芯片级能源效率为每个尖峰22个皮科朱尔.
- 通过层次地址事件路由实现灵活,分层和反复的突触连接.
- 通过数字校准缓解了突触强度的模拟不匹配问题.
结论:
- IFAT芯片代表了神经形态认知计算的重大进步.
- 它提供了一个高效和灵活的平台,用于大规模模拟生物物理尖端神经网络.
- 该系统支持详细的生物物理模拟和基于速率的神经激活映射.
- 这项技术有助于开发更复杂的人工智能系统,其灵感来源于生物大脑.
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