A Deep Dive into the Design Space of a Dynamically Reconfigurable Cryogenic Spiking Neuron

• URL: http://arxiv.org/abs/2308.15754v1
• Date: Wed, 30 Aug 2023 04:40:50 GMT
• Title: A Deep Dive into the Design Space of a Dynamically Reconfigurable Cryogenic Spiking Neuron
• Authors: Md Mazharul Islam, Shamiul Alam, Catherine D Schuman, Md Shafayat Hossain, Ahmedullah Aziz
• Abstract summary: Spiking neural network offers the most bio-realistic approach to mimic the parallelism and compactness of the human brain. A spiking neuron is the central component of an SNN which generates information-encoded spikes. We present a comprehensive design space analysis of the superconducting memristor (SM)-based electrically reconfigurable cryogenic neuron.
• Score: 1.4499463058550681
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking neural network offers the most bio-realistic approach to mimic the parallelism and compactness of the human brain. A spiking neuron is the central component of an SNN which generates information-encoded spikes. We present a comprehensive design space analysis of the superconducting memristor (SM)-based electrically reconfigurable cryogenic neuron. A superconducting nanowire (SNW) connected in parallel with an SM function as a dual-frequency oscillator and two of these oscillators can be coupled to design a dynamically tunable spiking neuron. The same neuron topology was previously proposed where a fixed resistance was used in parallel with the SNW. Replacing the fixed resistance with the SM provides an additional tuning knob with four distinct combinations of SM resistances, which improves the reconfigurability by up to ~70%. Utilizing an external bias current (Ibias), the spike frequency can be modulated up to ~3.5 times. Two distinct spike amplitudes (~1V and ~1.8 V) are also achieved. Here, we perform a systematic sensitivity analysis and show that the reconfigurability can be further tuned by choosing a higher input current strength. By performing a 500-point Monte Carlo variation analysis, we find that the spike amplitude is more variation robust than spike frequency and the variation robustness can be further improved by choosing a higher Ibias. Our study provides valuable insights for further exploration of materials and circuit level modification of the neuron that will be useful for system-level incorporation of the neuron circuit

Related papers

• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  In neuromorphic computing, spiking neural networks (SNNs) perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have demonstrated that embedding a few bits of payload in each spike exchanged between the spiking neurons can further enhance inference accuracy. This paper investigates a wireless neuromorphic split computing architecture employing multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Hardware-Friendly Implementation of Physical Reservoir Computing with CMOS-based Time-domain Analog Spiking Neurons [0.26963330643873434]
  This paper introduces a spiking neural network (SNN) for a hardware-friendly physical reservoir computing (RC) on a complementary metal-oxide-semiconductor (CMOS) platform. We demonstrate RC through short-term memory and exclusive OR tasks, and the spoken digit recognition task with an accuracy of 97.7%.
  arXiv  Detail & Related papers  (2024-09-18T00:23:00Z)
• Speed Limits for Deep Learning [67.69149326107103]
  Recent advancement in thermodynamics allows bounding the speed at which one can go from the initial weight distribution to the final distribution of the fully trained network. We provide analytical expressions for these speed limits for linear and linearizable neural networks. Remarkably, given some plausible scaling assumptions on the NTK spectra and spectral decomposition of the labels -- learning is optimal in a scaling sense.
  arXiv  Detail & Related papers  (2023-07-27T06:59:46Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)
• A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science [0.0]
  The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. The model is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing.
  arXiv  Detail & Related papers  (2023-06-03T03:36:47Z)
• Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks [72.09574528342732]
  Physics-informed neural network (PINN) algorithms have shown promising results in solving a wide range of problems involving partial differential equations (PDEs) They often fail to converge to desirable solutions when the target function contains high-frequency features, due to a phenomenon known as spectral bias. In the present work, we exploit neural tangent kernels (NTKs) to investigate the training dynamics of PINNs evolving under gradient descent with momentum (SGDM)
  arXiv  Detail & Related papers  (2022-06-29T19:03:10Z)
• A Fully Memristive Spiking Neural Network with Unsupervised Learning [2.8971214387667494]
  The system is fully memristive in that both neuronal and synaptic dynamics can be realized by using memristors. The proposed MSNN implements STDP learning by using cumulative weight changes in memristive synapses from the voltage waveform changes across the synapses.
  arXiv  Detail & Related papers  (2022-03-02T21:16:46Z)
• 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)
• Stability Analysis of Fractional Order Memristor Synapse-coupled Hopfield Neural Network with Ring Structure [0.0]
  We first present a fractional-order memristor synapse-coupling Hopfield neural network on two neurons. We extend the model to a neural network with a ring structure that consists of n sub-network neurons, increasing the synchronization in the network. In the n-neuron case, it is revealed that the stability depends on the structure and number of sub-networks.
  arXiv  Detail & Related papers  (2021-09-29T12:33:23Z)
• Spatiotemporal Spike-Pattern Selectivity in Single Mixed-Signal Neurons with Balanced Synapses [0.27998963147546135]
  Mixed-signal neuromorphic processors could be used for inference and learning. We show how inhomogeneous synaptic circuits could be utilized for resource-efficient implementation of network layers.
  arXiv  Detail & Related papers  (2021-06-10T12:04:03Z)
• Flexible Transmitter Network [84.90891046882213]
  Current neural networks are mostly built upon the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. We propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. We present the Flexible Transmitter Network (FTNet), which is built on the most common fully-connected feed-forward architecture.
  arXiv  Detail & Related papers  (2020-04-08T06:55:12Z)

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.