145 citations · 223 across the 11 of their papers we have counts for
12 papers
Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware
Pietro Bonazzi, Sizhen Bian, Giovanni Lippolis +3
This paper introduces a neuromorphic methodology for eye tracking, harnessing pure event data captured by a Dynamic Vision Sensor (DVS) camera. The framework integrates a directly…
Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
Jens E. Pedersen, Steven Abreu, Matthias Jobst +12
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine…
Ultra-low-power Image Classification on Neuromorphic Hardware
Gregor Lenz, Garrick Orchard, Sadique Sheik
Spiking neural networks (SNNs) promise ultra-low-power applications by exploiting temporal and spatial sparsity. The number of binary activations, called spikes, is proportional to…
Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline
Ole Richter, Yannan Xing, Michele De Marchi +9
Edge computing solutions that enable the extraction of high-level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smar…
EXODUS: Stable and Efficient Training of Spiking Neural Networks
Felix Christian Bauer, Gregor Lenz, Saeid Haghighatshoar +1
Spiking Neural Networks (SNNs) are gaining significant traction in machine learning tasks where energy-efficiency is of utmost importance. Training such networks using the state-of…
WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spotting
Philipp Weidel, Sadique Sheik
Ultra-low power local signal processing is a crucial aspect for edge applications on always-on devices. Neuromorphic processors emulating spiking neural networks show great computa…
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision
Julian Büchel, Gregor Lenz, Yalun Hu +2
Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications. Convolutional spiking neural networks mod…
Optimizing the energy consumption of spiking neural networks for neuromorphic applications
Martino Sorbaro, Qian Liu, Massimo Bortone +1
In the last few years, spiking neural networks have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a…
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Georgios Detorakis, Sadique Sheik, Charles Augustine +6
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large…
Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks
Hesham Mostafa, Bruno Pedroni, Sadique Sheik +1
Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significa…
Membrane-Dependent Neuromorphic Learning Rule for Unsupervised Spike Pattern Detection
Sadique Sheik, Somnath Paul, Charles Augustine +1
Several learning rules for synaptic plasticity, that depend on either spike timing or internal state variables, have been proposed in the past imparting varying computational capab…
Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity
Bruno U. Pedroni, Sadique Sheik, Siddharth Joshi +5
Spike-timing-dependent plasticity (STDP) incurs both causal and acausal synaptic weight updates, for negative and positive time differences between pre-synaptic and post-synaptic s…