Search Results for author:
Found 7 papers, 1 papers with code
Expanding memory in recurrent spiking networks
Recurrent spiking neural networks (RSNNs) are notoriously difficult to train because of the vanishing gradient problem that is enhanced by the binary nature of the spikes.
Hardware-aware Training Techniques for Improving Robustness of Ex-Situ Neural Network Transfer onto Passive TiO2 ReRAM Crossbars
Passive resistive random access memory (ReRAM) crossbar arrays, a promising emerging technology used for analog matrix-vector multiplications, are far superior to their active (1T1R) counterparts in terms of the integration density.
A tunable and versatile 28nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses
In this work we report a study and a co-design methodology of an analog SNN crossbar output circuit designed in a 28nm FD-SOI technology node that comprises a tunable current attenuator and a leak-integrate and fire neurons that would enable the integration of emerging non-volatile memories (eNVMs) for synaptic arrays based on various technologies including phase change (PCRAM), oxide-based (OxRAM), spin transfer and spin orbit torque magnetic memories (STT, SOT-MRAM).
Voltage-Dependent Synaptic Plasticity (VDSP): Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential
To validate the system-level performance of VDSP, we train a single-layer spiking neural network (SNN) for the recognition of handwritten digits.
Bio-inspired adaptive sensing through electropolymerization of organic electrochemical transistors
We further highlight how volumetric capacitance and effective mobility of PEDOT:PSS influence distinctively the transconductance and impedance of OECTs.
Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition
Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition.
Exploiting the Short-term to Long-term Plasticity Transition in Memristive Nanodevice Learning Architectures
This approach outperforms a conventional ELM-inspired system when the first layer is imprinted before training and testing, and especially so when variability in device timing evolution is considered: variability is therefore transformed from an issue to a feature.
