一个紧的,低功耗的 SoC 设计用于基于当前乘数电荷注入器突触的尖端神经网络.
Malik Summair Asghar1,2, Saad Arslan3, Ali A Al-Hamid1
1Department of Electronics, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种紧的模拟系统芯片 (SoC) 用于物联网设备中的低功耗尖端神经网络 (SNN). 新型设计实现了高精度,与数字同行相比,功耗显著降低.
科学领域:
- 神经形态工程的神经形态工程
- 集成电路设计 集成电路设计
- 人工智能 硬件 硬件
背景情况:
- 尖端神经网络 (SNN) 为人工智能任务提供节能计算.
- 低功耗的系统芯片 (SoC) 设计对于物联网 (IoT) 应用至关重要.
- 现有的SNN实现经常面临电力消耗和芯片面积方面的挑战.
研究的目的:
- 为低功耗物联网应用程序优化的SNN提供一个紧的模拟SoC实现.
- 开发新的模拟神经元和突触电路,以减少功率和面积.
- 为了证明拟议的SNN SoC的性能和效率与数字化实施相比.
主要方法:
- 设计了模拟神经元和突触电路,用于突触使用电流乘数电荷注入器 (CMCI),用于神经元使用异步结构.
- 使用65nmCMOS工艺实现了SNN SoC.
- 在MNIST数据集上训练SNN芯片,并将其性能 (面积,功率) 与基于FPGA的数字SoC进行比较.
主要成果:
- 拟议的突触电路 (CMCI) 减少了功耗和芯片面积,提供了设计可扩展性.
- 异步神经元电路增强对突触输入和能源效率的敏感性.
- 在MNIST数据集上,SNN芯片实现了96.56%的分类准确度.
- 制造出来的芯片占地0.96mm2,平均耗电量为530μW,比数字同行低200倍.
结论:
- 开发的模拟SNN SoC为物联网中的AI提供了高节能和紧的解决方案.
- 神经元和突触的新型电路设计使得功率和面积的显著减少.
- 这项工作证明了在资源受限的边缘设备上部署先进的人工智能能力的可行途径.
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