176 citations · 354 across the 9 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…