Hardware implementation of memristor-based SNN

Neural computation is assuming an increasing role for understanding the behavior of the brain and for emulating it in specific operations. The main goal in realizing analog VLSI  systems able to mimic functionalities of biological neural networks is related to obtain a sufficient performance for the following features: 1) biological neuron input-output behavior 2) realistic and compact synapses. 

About the first point, literature shows that reliable and precise latencies seem to have a basic role for the neural information representation. A very efficient neuron model exhibiting latency characteristics and a general good bio-plausibility was already presented by the authors in previous works. Now we propose a solution based on an analog circuit implementation. The resulting circuit shows a good matching with the ideal model and exhibits a relatively low complexity. The implementation uses a CMOS 90nm 6 Metal Copper low-K technology.

About the second point, we have to observe that in practical applications synapse/neuron ratio is very high, especially in the case of extremely dense networks (i.e., reproduction of a real scenario). For this reason synapses represent a considerable limitation in terms of waste of silicon area and power consumption. We propose a driving circuit model that does not require specific shape input pulses to change the memristor conductance (i.e., synaptic strength), but it can be driven by arbitrary shaped input pulses. Moreover, this prototype circuit offers the chance of emulating the standard STDP (Spike-Timing-Dependent Plasticity) behavior allowing “controlled” changes for the synaptic weights.

 

For this research area, ELTLAB collaborates with DSPVLSI group of the same Department.

 

LIST OF PUBLICATIONS 

CONFERENCE PAPERS  

Resistive Switching Behaviour In ZnO-rGO composite thin film

Gaurav Mani Khanal, Simone Acciarito, Gian Carlo Cardarilli, A. Chakraborty, Luca Di Nunzio, Rocco Fazzolari, Alessandro Cristini, Gianluca Susi, Marco Re

2017 Lecture Notes in Electrical Engineering: Applications in Electronics Pervading Industry, Environment and Society,  FORTHCOMING. Publisher: Springer International Publishing 

 

An aVLSI driving circuit for memristor-based STDP 

Simone Acciarito, Alessandro Cristini, Luca Di Nunzio, Gaurav Mani Khanal, Gianluca Susi

2016 IEEE 12th Conference on PhD Research In Microelectronics and Electronics (PRIME), Lisbon, Portugal, pp. 1-4, ISBN:978-1-5090-0493-5, DOI:10.1109/PRIME.2016.7519503

 

TECHNICAL REPORTS

Implementazione hardware di reti neurali spiking bio-realistiche, basate su memristore

Gian Carlo Cardarilli, Marco Re, Mario Salerno, Gianluca Susi, Alessandro Cristini, Simone Acciarito, Luca Di Nunzio, Rocco Fazzolari

2016 Technical Report ET2016. DOI:10.13140/RG.2.2.14030.87364

 

Hardware implementation of a spiking neuron model based on memristor

Simone Acciarito, Gian Carlo Cardarilli, Alessandro Cristini, Luca Di Nunzio, Rocco Fazzolari, Gaurav Mani Khanal, Marco Re, Gianluca Susi

2016 Technical Report GE2016.