Integrated Artificial Neurons from Metal Halide Perovskites

• URL: http://arxiv.org/abs/2411.19820v1
• Date: Fri, 29 Nov 2024 16:30:23 GMT
• Title: Integrated Artificial Neurons from Metal Halide Perovskites
• Authors: Jeroen J. de Boer, Bruno Ehrler,
• Abstract summary: Hardware neural networks could perform certain computational tasks orders of magnitude more energy-efficiently than conventional computers. artificial neurons are a key component of these networks and are currently implemented with electronic circuits based on capacitors and transistors. Here we demonstrate a fully on-chip artificial neuron based on microscale electrodes and perovskite semiconductors.
• Score: 0.0
• License:
• Abstract: Hardware neural networks could perform certain computational tasks orders of magnitude more energy-efficiently than conventional computers. Artificial neurons are a key component of these networks and are currently implemented with electronic circuits based on capacitors and transistors. However, artificial neurons based on memristive devices are a promising alternative, owing to their potentially smaller size and inherent stochasticity. But despite their promise, demonstrations of memristive artificial neurons have so far been limited. Here we demonstrate a fully on-chip artificial neuron based on microscale electrodes and halide perovskite semiconductors as the active layer. By connecting a halide perovskite memristive device in series with a capacitor, the device demonstrates stochastic leaky integrate-and-fire behavior, with an energy consumption of 20 to 60 pJ per spike, lower than that of a biological neuron. We simulate populations of our neuron and show that the stochastic firing allows the detection of sub-threshold inputs. The neuron can easily be integrated with previously-demonstrated halide perovskite artificial synapses in energy-efficient neural networks.

Related papers

• Threshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference [17.95548501630064]
  We propose a novel artificial neuron model, Threshold Neurons. We construct neural networks similar to those with traditional artificial neurons, while significantly reducing hardware implementation complexity. Our experiments validate the effectiveness of neural networks utilizing Threshold Neurons, achieving substantial power savings of 7.51x to 8.19x and area savings of 3.89x to 4.33x at the kernel level, with minimal loss in precision.
  arXiv  Detail & Related papers  (2024-12-18T14:42:43Z)
• Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
  We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
  arXiv  Detail & Related papers  (2024-02-26T15:01:54Z)
• A versatile circuit for emulating active biological dendrites applied to sound localisation and neuron imitation [0.0]
  We introduce a versatile circuit that emulates a segment of a dendrite which exhibits gain, introduces delays, and performs integration. We also find that dendrites can form bursting neurons. This significant discovery suggests the potential to fabricate neural networks solely comprised of dendrite circuits.
  arXiv  Detail & Related papers  (2023-10-25T09:42:24Z)
• Parametrized constant-depth quantum neuron [56.51261027148046]
  We propose a framework that builds quantum neurons based on kernel machines. We present here a neuron that applies a tensor-product feature mapping to an exponentially larger space. It turns out that parametrization allows the proposed neuron to optimally fit underlying patterns that the existing neuron cannot fit.
  arXiv  Detail & Related papers  (2022-02-25T04:57:41Z)
• An Adiabatic Capacitive Artificial Neuron with RRAM-based Threshold Detection for Energy-Efficient Neuromorphic Computing [62.997667081978825]
  We present an artificial neuron featuring adiabatic synapse capacitors to produce membrane potentials for the somas of neurons. Our initial 4-bit adiabatic capacitive neuron proof-of-concept example shows 90% synaptic energy saving.
  arXiv  Detail & Related papers  (2022-02-02T17:12:22Z)
• POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with Integer Quadratic Integrate-and-Fire Neurons [50.591267188664666]
  We propose a population-based digital spiking neuromorphic processor in 180nm process technology with two hierarchy populations. The proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.
  arXiv  Detail & Related papers  (2022-01-19T09:26:34Z)
• Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi [77.34726150561087]
  We investigate the potential of Intel's fifth generation neuromorphic chip - Loihi' Loihi is based on the novel idea of Spiking Neural Networks (SNNs) emulating the neurons in the brain. We find that Loihi replicates classical simulations very efficiently and scales notably well in terms of both time and energy performance as the networks get larger.
  arXiv  Detail & Related papers  (2021-09-22T16:52:51Z)
• Continual Learning with Deep Artificial Neurons [0.0]
  We introduce Deep Artificial Neurons (DANs), which are themselves realized as deep neural networks. We demonstrate that it is possible to meta-learn a single parameter vector, which we dub a neuronal phenotype, shared by all DANs in the network. We show that a suitable neuronal phenotype can endow a single network with an innate ability to update its synapses with minimal forgetting.
  arXiv  Detail & Related papers  (2020-11-13T17:50:10Z)
• Non-linear Neurons with Human-like Apical Dendrite Activations [81.18416067005538]
  We show that a standard neuron followed by our novel apical dendrite activation (ADA) can learn the XOR logical function with 100% accuracy. We conduct experiments on six benchmark data sets from computer vision, signal processing and natural language processing.
  arXiv  Detail & Related papers  (2020-02-02T21:09:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.