A Generalized Strong-Inversion CMOS Circuitry for Neuromorphic Applications

• URL: http://arxiv.org/abs/2007.13941v2
• Date: Fri, 6 Aug 2021 17:30:26 GMT
• Title: A Generalized Strong-Inversion CMOS Circuitry for Neuromorphic Applications
• Authors: Hamid Soleimani and Emmanuel. M. Drakakis
• Abstract summary: It has always been a challenge in the neuromorphic field to systematically translate biological models into analog electronic circuitry. In this paper, a generalized circuit design platform is introduced where biological models can be conveniently implemented using CMOS circuitry. The validity of our approach is verified by nominal simulated results with realistic process parameters from the commercially available AMS 0.35 um technology.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: It has always been a challenge in the neuromorphic field to systematically translate biological models into analog electronic circuitry. In this paper, a generalized circuit design platform is introduced where biological models can be conveniently implemented using CMOS circuitry operating in strong-inversion. The application of the method is demonstrated by synthesizing a relatively complex two-dimensional (2-D) nonlinear neuron model. The validity of our approach is verified by nominal simulated results with realistic process parameters from the commercially available AMS 0.35 um technology. The circuit simulation results exhibit regular spiking responses in good agreement with their mathematical counterpart.

Related papers

• A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures [73.65190161312555]
  ARCANA is a spiking neural network simulator designed to account for the properties of mixed-signal neuromorphic circuits. We show how the results obtained provide a reliable estimate of the behavior of the spiking neural network trained in software.
  arXiv  Detail & Related papers  (2024-09-23T11:16:46Z)
• LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits [17.002169206594793]
  We introduce LaMAGIC, a pioneering language model-based topology generation model. LaMAGIC can efficiently generate an optimized circuit design from the custom specification in a single pass. LaMAGIC achieves a success rate of up to 96% under a strict tolerance of 0.01.
  arXiv  Detail & Related papers  (2024-07-19T22:51:41Z)
• Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures [0.8192907805418581]
  We develop a parsimonious Hammerstein model for a non-linear CMOS differential amplifier. We train this model on a combination of direct current (DC) and transient Spice (Xyce) circuit simulation data. Simulation results show that the Hammerstein model is an effective surrogate for the differential amplifier circuit.
  arXiv  Detail & Related papers  (2024-05-30T15:47:48Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Conditional Generative Models for Simulation of EMG During Naturalistic Movements [45.698312905115955]
  We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
  arXiv  Detail & Related papers  (2022-11-03T14:49:02Z)
• Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits [11.636872461683742]
  This paper proposes a class of neural ordinary differential equations parametrized by provably input-to-state stable continuous-time recurrent neural networks. We use the proposed method to learn cheap-to-simulate behavioral models for electronic circuits.
  arXiv  Detail & Related papers  (2022-02-14T01:51:05Z)
• Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks [0.0]
  We explore different machine learning (ML) methodologies to increase the resolution of molecular dynamics trajectories on-demand within a post-processing step. We have found that Bi-LSTMs are the best performing models; by utilizing the local time-symmetry of thermostated trajectories they can even learn long-range correlations and display high robustness to noisy dynamics across molecular complexity.
  arXiv  Detail & Related papers  (2022-01-02T23:00:30Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach [144.21892195917758]
  We study estimation in a class of generalized Structural equation models (SEMs) We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using a gradient descent. For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.
  arXiv  Detail & Related papers  (2020-07-02T17:55:47Z)
• Efficient classical simulation of random shallow 2D quantum circuits [104.50546079040298]
  Random quantum circuits are commonly viewed as hard to simulate classically. We show that approximate simulation of typical instances is almost as hard as exact simulation. We also conjecture that sufficiently shallow random circuits are efficiently simulable more generally.
  arXiv  Detail & Related papers  (2019-12-31T19:00:00Z)

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.