A 0.086-mm 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS
Shifting computing architectures from von Neumann to event-based spiking neural networks
(SNNs) uncovers new opportunities for low-power processing of sensory data in …
(SNNs) uncovers new opportunities for low-power processing of sensory data in …
Spiking neural networks hardware implementations and challenges: A survey
M Bouvier, A Valentian, T Mesquida… - ACM Journal on …, 2019 - dl.acm.org
Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …
Bottom-up and top-down approaches for the design of neuromorphic processing systems: tradeoffs and synergies between natural and artificial intelligence
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 …
calls for new avenues for improving the overall system performance. One of these avenues …
SpiNNaker 2: A 10 million core processor system for brain simulation and machine learning-Keynote presentation
SpiNNaker is an ARM-based processor platform optimized for the simulation of spiking
neural networks. This brief describes the roadmap in going from the current SPINNaker1 …
neural networks. This brief describes the roadmap in going from the current SPINNaker1 …
Bottom-up and top-down neural processing systems design: Neuromorphic intelligence as the convergence of natural and artificial intelligence
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 …
calls for new avenues for improving the overall system performance. One of these avenues …
[HTML][HTML] GPUs outperform current HPC and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model
While neuromorphic systems may be the ultimate platform for deploying spiking neural
networks (SNNs), their distributed nature and optimization for specific types of models …
networks (SNNs), their distributed nature and optimization for specific types of models …
The SpiNNaker 2 processing element architecture for hybrid digital neuromorphic computing
This paper introduces the processing element architecture of the second generation
SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive …
SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive …
Comparing Loihi with a SpiNNaker 2 prototype on low-latency keyword spotting and adaptive robotic control
We implemented two neural network based benchmark tasks on a prototype chip of the
second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and …
second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and …
Memory-efficient deep learning on a SpiNNaker 2 prototype
The memory requirement of deep learning algorithms is considered incompatible with the
memory restriction of energy-efficient hardware. A low memory footprint can be achieved by …
memory restriction of energy-efficient hardware. A low memory footprint can be achieved by …
Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
Recurrent neural networks (RNNs) are well suited for solving sequence tasks in resource-
constrained systems due to their expressivity and low computational requirements …
constrained systems due to their expressivity and low computational requirements …