331 citations · 862 across the 12 of their papers we have counts for
13 papers
Online Spatio-Temporal Learning with Target Projection
Thomas Ortner, Lorenzo Pes, Joris Gentinetta +2
Recurrent neural networks trained with the backpropagation through time (BPTT) algorithm have led to astounding successes in various temporal tasks. However, BPTT introduces severe…
THOR -- A Neuromorphic Processor with 7.29G TSOP/mmJs Energy-Throughput Efficiency
Mayank Senapati, Manil Dev Gomony, Sherif Eissa +2
Neuromorphic computing using biologically inspired Spiking Neural Networks (SNNs) is a promising solution to meet Energy-Throughput (ET) efficiency needed for edge computing device…
ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales
Charlotte Frenkel, Giacomo Indiveri
A robust real-world deployment of autonomous edge devices requires on-chip adaptation to user-, environment- and task-induced variability. Due to on-chip memory constraints, prior…
Spiking Neural Network Integrated Circuits: A Review of Trends and Future Directions
Arindam Basu, Charlotte Frenkel, Lei Deng +1
In this paper, we reviewed Spiking neural network (SNN) integrated circuit designs and analyzed the trends among mixed-signal cores, fully digital cores and large-scale, multi-core…
A 120dB Programmable-Range On-Chip Pulse Generator for Characterizing Ferroelectric Devices
Shyam Narayanan, Erika Covi, Viktor Havel +7
Novel non-volatile memory devices based on ferroelectric thin films represent a promising emerging technology that is ideally suited for neuromorphic applications. The physical swi…
Stochastic dendrites enable online learning in mixed-signal neuromorphic processing systems
Matteo Cartiglia, Arianna Rubino, Shyam Narayanan +4
The stringent memory and power constraints required in edge-computing sensory-processing applications have made event-driven neuromorphic systems a promising technology. On-chip on…
Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses
Yigit Demirag, Charlotte Frenkel, Melika Payvand +1
Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse proces…
Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence
Charlotte Frenkel, David Bol, Giacomo Indiveri
While Moore's law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is t…
PCM-trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials
Yigit Demirag, Filippo Moro, Thomas Dalgaty +5
Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the…
A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas
Charlotte Frenkel, Jean-Didier Legat, David Bol
In an attempt to follow biological information representation and organization principles, the field of neuromorphic engineering is usually approached bottom-up, from the biophysic…
Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks
Charlotte Frenkel, Martin Lefebvre, David Bol
While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward…
MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning
Charlotte Frenkel, Jean-Didier Legat, David Bol
Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-…
A 0.086-mm 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28nm CMOS
Charlotte Frenkel, Martin Lefebvre, Jean-Didier Legat +1
Shifting computing architectures from von Neumann to event-based spiking neural networks (SNNs) uncovers new opportunities for low-power processing of sensory data in applications…