A CMOL-Like Memristor-CMOS Neuromorphic Chip-Core Demonstrating Stochastic Binary STDP

Abstract

The advent of nanoscale memristors raised hopes of being able to build CMOL (CMOS/nanowire/molecular) type ultra-dense in-memory-computing circuit architectures. In CMOL, nanoscale memristors would be fabricated at the intersection of nanowires. The CMOL concept can be exploited in neuromorphic hardware by fabricating lower density neurons on CMOS and placing massive analog synaptic connectivity with nanowire and nanoscale-memristor fabric post-fabricated on top. However, technical problems have hindered such developments for presently available reliable commercial monolithic CMOSmemristor technologies. On one hand, each memristor needs a MOS selector transistor in series to guarantee forming and programming operations in large arrays. This results in compound MOS-memristor synapses (called 1T1R) which are no longer synapses at the crossing of nanowires. On the other hand, memristors do not yet constitute highly reliable, stable analog memories for massive analog-weight synapses with gradual learning. Here we demonstrate a pseudo-CMOL monolithic chip core that circumvents the two technical problems mentioned above by: (a) exploiting a CMOL-like geometrical chip layout technique to improve density despite the 1T1R limitation, and (b) exploiting a binary weight stochastic Spike-Timing-Dependent-Plasticity (STDP) learning rule that takes advantage of the more reliable binary memory capability of the memristors used. Experimental results are provided for a spiking neural network (SNN) CMOL-core with 64 input neurons, 64 output neurons and 4096 1T1R synapses, fabricated in 130nm CMOS with 200nm-sized Ti/HfOx/TiN memristors on top. The CMOL-core uses query-driven event read-out, which allows for memristor variability insensitive computations. Experimental system-level demonstrations are provided for plain template matching tasks, as well as regularized stochastic binary STDP feature-extraction learning, obtaining perfect recognition in hardware for a 4-letter recognition experiment.

Thermal Characterization of Conductive Filaments in Unipolar Resistive Memories

“…Resistive memories can be employed in a digital context, where they show two stable resistive states: the high resistance state (HRS) and the low resistance state (LRS); moreover, an analog perspective is appropriate for neuromorphic computing applications [ 1 , 7 ], although their usually exhibited non-linear response could make the weight update more difficult [ 25 ]. However, even devices that exhibit abrupt (digital) switching between the HRS and LRS can still be used for the implementation of binarized neural networks [ 26 ] or binary weight spike-time-dependent plasticity (STDP) learning rules [ 27 ].…”