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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

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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

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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

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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

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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

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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.

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Very large scale neuromorphic systems for biological signal processing

F.Catthoor, S.Mitra, A.Das, S.Schaafsma, Chapter in ``CMOS circuits for biological sensing and processing'' (eds. J.Cummings, S.Mitra), ISBN 978-3-319-67723-1, Springer, Heidelberg, Germany, Jan. 2018.

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Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

A.Das, P.Pradhapan, W.Groenendaal, P.Adiraju, R.T.Rajan, F.Catthoor, S.Schaafsma, J.L.Krichmar, N.Dutt, C.Van Hoof, CoRR, http://arxiv.org/abs/1708.05356, 2017. Accepted for Neural Networks, 2018.

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Mapping of Local and Global Synapses on Spiking Neuromorphic Hardware

A.Das, Y.Wu, K.Huynh, F.Dell Anna, F.Catthoor and S.Schaafsma, accepted for Proc. 21th ACM/IEEE Design and Test in Europe Conf.(DATE), Dresden, Germany, March 2018.

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Reconfigurable neuromorphic synapse interconnects with TFTs

J.Genoe, P.Raghavan, Z.Tokei, F.Catthoor, S.Steudel, Proc. 46th Europ. Solid-state Device Conf.(ESSDERC), Lausanne, Switzerland, pp.69-, Sep. 2016.

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Reconfigurable neuromorphic synapse interconnects with TFT

S.Steudel, K.Myny, J.Stuijt, F.Catthoor, 1st Neuromorphic computing wsh., Grenoble, France, Nov. 2017. (only presentation)

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An event-based classifier for dynamic vision sensor and synthetic data

E. Stromatias, M. Soto, T. Serrano-Gotarredona, and B. Linares-Barranco This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST dataset using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips. Open access from: https://www.frontiersin.org/articles/10.3389/fnins.2017.00350/full

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