A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models

• URL: http://arxiv.org/abs/2201.02262v1
• Date: Thu, 6 Jan 2022 22:02:19 GMT
• Title: A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models
• Authors: Artem R. Muliukov, Laurent Rodriguez, Benoit Miramond, Lyes Khacef, Joachim Schmidt, Quentin Berthet, Andres Upegui
• Abstract summary: We develop an original brain-inspired neural model associating Self-Organizing Maps (SOM) and Hebbian learning in the Reentrant SOM (ReSOM) model. This work also demonstrates the distributed and scalable nature of the model through both simulation results and hardware execution on a dedicated FPGA-based platform.
• Score: 6.072718806755325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The field of artificial intelligence has significantly advanced over the past decades, inspired by discoveries from the fields of biology and neuroscience. The idea of this work is inspired by the process of self-organization of cortical areas in the human brain from both afferent and lateral/internal connections. In this work, we develop an original brain-inspired neural model associating Self-Organizing Maps (SOM) and Hebbian learning in the Reentrant SOM (ReSOM) model. The framework is applied to multimodal classification problems. Compared to existing methods based on unsupervised learning with post-labeling, the model enhances the state-of-the-art results. This work also demonstrates the distributed and scalable nature of the model through both simulation results and hardware execution on a dedicated FPGA-based platform named SCALP (Self-configurable 3D Cellular Adaptive Platform). SCALP boards can be interconnected in a modular way to support the structure of the neural model. Such a unified software and hardware approach enables the processing to be scaled and allows information from several modalities to be merged dynamically. The deployment on hardware boards provides performance results of parallel execution on several devices, with the communication between each board through dedicated serial links. The proposed unified architecture, composed of the ReSOM model and the SCALP hardware platform, demonstrates a significant increase in accuracy thanks to multimodal association, and a good trade-off between latency and power consumption compared to a centralized GPU implementation.

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)
• Deep Learning of Dynamic Systems using System Identification Toolbox(TM) [1.7537651436360189]
  The toolbox offers neural state-space models which can be extended with auto-encoding features. The toolbox contains several other enhancements that deepen its integration with the state-of-art machine learning techniques.
  arXiv  Detail & Related papers  (2024-09-11T21:54:17Z)
• Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysis [1.7329715392023939]
  We introduce functional functional Mamba (FST-Mamba), a Mamba-based model designed for discovering neurological biomarkers using fMRI. We propose a component-wise varied-scale aggregation (CVA) mechanism to aggregate connectivity across individual components within brain networks. Experimental results demonstrate significant improvements in the proposed FST-Mamba model on various brain-based classification and regression tasks.
  arXiv  Detail & Related papers  (2024-08-23T13:58:14Z)
• Flemme: A Flexible and Modular Learning Platform for Medical Images [5.086862917025204]
  Flemme is a FLExible and Modular learning platform for MEdical images. We construct encoders using building blocks based on convolution, transformer, and state-space model (SSM) to process both 2D and 3D image patches.
  arXiv  Detail & Related papers  (2024-08-18T05:47:33Z)
• A differentiable programming framework for spin models [0.0]
  We introduce a novel framework for simulating spin models using differentiable programming. We focus on three distinct spin systems: the Ising model, the Potts model, and the Cellular Potts model.
  arXiv  Detail & Related papers  (2023-04-04T13:04:21Z)
• Neural Attentive Circuits [93.95502541529115]
  We introduce a general purpose, yet modular neural architecture called Neural Attentive Circuits (NACs) NACs learn the parameterization and a sparse connectivity of neural modules without using domain knowledge. NACs achieve an 8x speedup at inference time while losing less than 3% performance.
  arXiv  Detail & Related papers  (2022-10-14T18:00:07Z)
• Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks [53.09649785009528]
  In this paper, we explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we propose Model Disassembling and Assembling. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task.
  arXiv  Detail & Related papers  (2022-03-25T05:27:28Z)
• A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware [0.0]
  This work presents the software aspects of the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development.
  arXiv  Detail & Related papers  (2022-03-21T16:30:18Z)
• Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms [59.03426963238452]
  We present Real-time Neural MPC, a framework to efficiently integrate large, complex neural network architectures as dynamics models within a model-predictive control pipeline. We show the feasibility of our framework on real-world problems by reducing the positional tracking error by up to 82% when compared to state-of-the-art MPC approaches without neural network dynamics.
  arXiv  Detail & Related papers  (2022-03-15T09:38:15Z)
• Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition [140.18376685167857]
  A simple yet effective multi-scale semantics-guided neural network is proposed for skeleton-based action recognition. MS-SGN achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets.
  arXiv  Detail & Related papers  (2021-11-07T03:50:50Z)
• S2RMs: Spatially Structured Recurrent Modules [105.0377129434636]
  We take a step towards exploiting dynamic structure that are capable of simultaneously exploiting both modular andtemporal structures. We find our models to be robust to the number of available views and better capable of generalization to novel tasks without additional training.
  arXiv  Detail & Related papers  (2020-07-13T17:44:30Z)

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.