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Analog Circuits for Mixed-Signal Neuromorphic Computing Architectures in 28 nm FD-SOI Technology

Ning Qiao and Giacomo Indiveri, Automatic Gain Control of Ultra-Low Leakage Synaptic Scaling Homeostatic Plasticity Circuits, Biomedical Circuits and Systems (BIOCAS) 2016

Analog Circuits for Mixed-Signal Neuromorphic Computing Architectures in 28 nm FD-SOI Technology - Read More…

Automatic Gain Control of Ultra-Low Leakage Synaptic Scaling Homeostatic Plasticity Circuits

Ning Qiao, Giacomo Indiveri, Chiara Bartolozzi, Automatic Gain Control of Ultra-Low Leakage Synaptic Scaling Homeostatic Plasticity Circuits, Biomedical Circuits and Systems (BIOCAS) 2016

Automatic Gain Control of Ultra-Low Leakage Synaptic Scaling Homeostatic Plasticity Circuits - Read More…

An Auto-Scaling Wide Dynamic Range Current to Frequency Converter for Real-Time Monitoring of Signals in Neuromorphic Systems

Ning Qiao and Giacomo Indiveri, An Auto-Scaling Wide Dynamic Range Current to Frequency Converter for Real-Time Monitoring of Signals in Neuromorphic Systems Biomedical Circuits and Systems (BIOCAS) 2016

An Auto-Scaling Wide Dynamic Range Current to Frequency Converter for Real-Time Monitoring of Signals in Neuromorphic Systems - Read More…

Scaling Mixed-Signal Neuromorphic Processors to 28 nm FD-SOI Technologies

Ning Qiao and Giacomo Indiveri, Automatic Gain Control of Ultra-Low Leakage Synaptic Scaling Homeostatic Plasticity Circuits , Biomedical Circuits and Systems (BIOCAS) 2016

Scaling Mixed-Signal Neuromorphic Processors to 28 nm FD-SOI Technologies - Read More…

An Ultralow Leakage Synaptic Scaling Homeostatic Plasticity Circuit With Configurable Time Scales up to 100 ks

Ning Qiao, Chiara Bartolozzi, Giacomo Indiver, An Ultralow Leakage Synaptic Scaling Homeostatic Plasticity Circuit With Configurable Time Scales up to 100 ks, IEEE Transactions on Biomedical Circuits and Systems, PP:(99), 2017

An Ultralow Leakage Synaptic Scaling Homeostatic Plasticity Circuit With Configurable Time Scales up to 100 ks - Read More…

A differential memristive synapse circuit for on-line learning in neuromorphic computing systems

Manu V Nair, Lorenz K Muller and Giacomo Indiveri Published 17 November 2017 • © 2017 IOP Publishing Ltd Nano Futures, Volume 1, Number 3 Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes. This imposes severe constraints on the length of the pulses transmitted in the network, and on the network’s throughput. Furthermore, most of these circuits do not decouple the currents flowing through memristive devices from the one stimulating the target neuron. This can be a problem when using devices with high conductance values, because of the resulting large currents. In this paper, we propose a novel circuit that decouples the current produced by the memristive device from the one used to stimulate the post-synaptic neuron, by using a novel differential scheme based on the Gilbert normalizer circuit. We show how this circuit is useful for reducing the effect of variability in the memristive devices, and how it is ideally suited for spike-based learning mechanisms that do not require overlapping pre- and post-synaptic pulses. We demonstrate the features of the proposed synapse circuit with SPICE simulations, and validate its learning properties with high-level behavioral network simulations which use a stochastic gradient descent learning rule in two benchmark classification tasks.

A differential memristive synapse circuit for on-line learning in neuromorphic computing systems - Read More…