An Accelerated LIF Neuronal Network Array for a Large Scale Mixed-Signal Neuromorphic Architecture
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Abstract (arXiv)
We present an array of leaky integrate-and-fire (LIF) neuron circuits designed for the second-generation BrainScaleS mixed-signal 65-nm CMOS neuromorphic hardware. The neuronal array is embedded in the analog network core of a scaled-down prototype HICANN-DLS chip. Designed as continuous-time circuits, the neurons are highly tunable and reconfigurable elements with accelerated dynamics. Each neuron integrates input current from a multitude of incoming synapses and evokes a digital spike event output. The circuit offers a wide tuning range for synaptic and membrane time constants, as well as for refractory periods to cover a number of computational models. We elucidate our design methodology, underlying circuit design, calibration and measurement results from individual sub-circuits across multiple dies. The circuit dynamics match with the behavior of the LIF mathematical model. We further demonstrate a winner-take-all network on the prototype chip as a typical element of cortical processing.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1.
5 papers.
- 2021Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligencecited by 21
- 2021Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processorscited by 7
- 2022Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenariocited by 6
- 2023Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron modelscited by 4
- 2021Demonstrating Analog Inference on the BrainScaleS-2 Mobile Systemcited by 1
- 2023Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models26 Apr 2023
- 2022Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenario28 Jun 2022
- 2021Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence2 Jun 2021
- 2021Demonstrating Analog Inference on the BrainScaleS-2 Mobile System29 Mar 2021
- 2021Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors12 Feb 2021