Hybrid Cmos-Rram Neurons With Intrinsic Plasticity
2019 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)(2019)
摘要
Brain-inspired architectures in neuromorphic hardware are currently subject to intensive research as an alternative to the limits of traditional computer organisation. The remarkable computing performance and efficiency of biological nervous systems are widely attributed to the co-localisation of memory and computation spatially throughout the structure. Moreover, it appears that a number of local self-organising neural mechanisms play their part in efficient biological computation. An example is neuronal intrinsic plasticity, where a neuron adapts its parameters to maximise its information capacity based on the statistical properties of its input while minimising the power it consumes. CMOS circuits implementing neuron models have been proposed but require their parameters to be set by biases originating from a centralised memory. In this work, we propose a hybrid CMOS-RRAM circuit that addresses this problem through storing neuron parameters within programmable nonvolatile resistive memories incorporated into the CMOS neuron. Additional circuits exploit the stochastic switching properties of resisitive memories to map a local intrinsic plasticity algorithm onto the proposed neuron. We demonstrate the computational advantages of this algorithm through simulation, calibrated on experimental data, whereby the neuron maximises its information capacity while minimising its power consumption, as is the case for biological neurons.
更多查看译文
关键词
hybrid CMOS-RRAM neurons,brain-inspired architectures,neuromorphic hardware,biological nervous systems,neuronal intrinsic plasticity,CMOS circuits,neuron models,centralised memory,hybrid CMOS-RRAM circuit,programmable nonvolatile resistive memories,CMOS neuron,stochastic switching properties,resisitive memories,local intrinsic plasticity algorithm,biological neurons,self-organising neural mechanisms
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络