Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power solutions for edge-computing sensory-processing applications, thanks to their ability to emulate spiking neural networks in real-time. The fine-grain parallelism offered by this approach allows such neural circuits to process the sensory data efficiently by adapting their dynamics to the ones of the sensed signals, without having to resort to the time-multiplexed computing paradigm of von Neumann architectures. To reduce power consumption even further, we present a set of mixed-signal analog/digital circuits that exploit the features of advanced Fully-Depleted Silicon on Insulator (FDSOI) integration processes. Specifically, we explore the options of advanced FDSOI technologies to address analog design issues and optimize the design of the synapse integrator and of the adaptive neuron circuits accordingly. We present circuit simulation results and demonstrate the circuit's ability to produce biologically plausible neural dynamics with compact designs, optimized for the realization of large-scale spiking neural networks in neuromorphic processors.

中文翻译：

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用于极端边缘神经形态智能的超低功耗 FDSOI 神经电路

近年来，人们对用于边缘计算应用的人工智能电路和系统的开发越来越感兴趣。内存计算混合信号神经形态架构为边缘计算传感处理应用提供了有前途的超低功耗解决方案，这要归功于它们实时模拟尖峰神经网络的能力。这种方法提供的细粒度并行性允许这种神经电路通过使它们的动态适应感测信号的动态来有效地处理感测数据，而不必求助于冯诺依曼架构的时分复用计算范式。为了进一步降低功耗，我们提出了一组混合信号模拟/数字电路，这些电路利用了先进的全耗尽绝缘体上硅 (FDSOI) 集成工艺的特性。具体而言，我们探索了高级 FDSOI 技术的选项，以解决模拟设计问题并相应地优化突触积分器和自适应神经元电路的设计。我们展示了电路仿真结果，并展示了该电路能够通过紧凑的设计产生生物学上合理的神经动力学，并针对在神经形态处理器中实现大规模尖峰神经网络进行了优化。