Analog circuits for mixed-signal neuromorphic computing architectures in 28 nm FD-SOI technology
2017 IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conference (S3S)(2017)
摘要
Developing mixed-signal analog-digital neuromorphic circuits in advanced scaled processes poses significant design challenges. We present compact and energy efficient sub-threshold analog synapse and neuron circuits, optimized for a 28 nm FD-SOI process, to implement massively parallel large-scale neuromorphic computing systems. We describe the techniques used for maximizing density with mixed-mode analog/digital synaptic weight configurations, and the methods adopted for minimizing the effect of channel leakage current, in order to implement efficient analog computation based on pA-nA small currents. We present circuit simulation results, based on a new chip that has been recently taped out, to demonstrate how the circuits can be useful for both low-frequency operation in systems that need to interact with the environment in real-time, and for high-frequency operation for fast data processing in different types of spiking neural network architectures.
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关键词
Sub-threshold analog,neuromorphic computing,low leakage,spiking neural networks,low power,IoT,ReLU
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