The hardware realization of spiking neural network (SNN) requires a compact and energy efficient electronic analog to the biological neuron. A knob to tune the response of the as-fabricated neuron allows the network to perform various functioning without altering the hardware. Earlier, our group has experimentally demonstrated an LIF (leaky integrate & fire) neuron on a highly matured 32?nm SOI CMOS technology. In this work, we have experimentally demonstrated electrical tunability of the same through its intrinsic charge dynamics based on impact ionization (II) enabled floating body effect. First, a tunable input threshold (Vth) is achieved by changing the drain bias. Second, above threshold, a firing frequency (f) to input (V) sensitivity...
With the continuous development of deep learning, the scientific community continues to propose new ...
Spiking Neural Networks (SNNs) are becoming increasingly popular for their application in Edge Artif...
We present an analogue Very Large Scale Integration (aVLSI) implementation that uses first-order log...
Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks...
Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks...
Spiking Neural Networks propose to mimic nature’s way of recognizing patterns and making decisions i...
Variability is an integral part of biology. A biological neural network performs efficiently despite...
Variability is an integral part of biology. A biological neural network performs efficiently despite...
The human brain comprises about a hundred billion neurons connected through quadrillion synapses. Sp...
International audienceThe emergence of new hardware-oriented algorithms for convolutional neural net...
Abstract. We describe an improved spiking silicon neuron (SN) [6] that approximates the dynamics of ...
Cortical circuits in the brain have long been recognised for their information processing capabiliti...
Ben Dayan Rubin D, Chicca E, Indiveri G. Firing proprieties of an adaptive analog VLSI neuron. Prese...
Cortical circuits in the brain have long been recognised for their information processing capabiliti...
Neural processing systems typically represent data using Leaky Integrate and Fire (LIF) neuron model...
With the continuous development of deep learning, the scientific community continues to propose new ...
Spiking Neural Networks (SNNs) are becoming increasingly popular for their application in Edge Artif...
We present an analogue Very Large Scale Integration (aVLSI) implementation that uses first-order log...
Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks...
Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks...
Spiking Neural Networks propose to mimic nature’s way of recognizing patterns and making decisions i...
Variability is an integral part of biology. A biological neural network performs efficiently despite...
Variability is an integral part of biology. A biological neural network performs efficiently despite...
The human brain comprises about a hundred billion neurons connected through quadrillion synapses. Sp...
International audienceThe emergence of new hardware-oriented algorithms for convolutional neural net...
Abstract. We describe an improved spiking silicon neuron (SN) [6] that approximates the dynamics of ...
Cortical circuits in the brain have long been recognised for their information processing capabiliti...
Ben Dayan Rubin D, Chicca E, Indiveri G. Firing proprieties of an adaptive analog VLSI neuron. Prese...
Cortical circuits in the brain have long been recognised for their information processing capabiliti...
Neural processing systems typically represent data using Leaky Integrate and Fire (LIF) neuron model...
With the continuous development of deep learning, the scientific community continues to propose new ...
Spiking Neural Networks (SNNs) are becoming increasingly popular for their application in Edge Artif...
We present an analogue Very Large Scale Integration (aVLSI) implementation that uses first-order log...