Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch

• URL: http://arxiv.org/abs/2302.07624v1
• Date: Wed, 15 Feb 2023 12:35:53 GMT
• Title: Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch
• Authors: Davide Liberato Manna, Alex Vicente-Sola, Paul Kirkland, Trevor Joseph Bihl, Gaetano Di Caterina
• Abstract summary: Spiking Neural Networks (SNNs) are specifically oriented towards data science applications. This work reviews 9 frameworks for the development of Spiking Neural Networks (SNNs) that are specifically oriented towards data science applications.
• Score: 0.3425341633647624
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Developing effective learning systems for Machine Learning (ML) applications in the Neuromorphic (NM) field requires extensive experimentation and simulation. Software frameworks aid and ease this process by providing a set of ready-to-use tools that researchers can leverage. The recent interest in NM technology has seen the development of several new frameworks that do this, and that add up to the panorama of already existing libraries that belong to neuroscience fields. This work reviews 9 frameworks for the development of Spiking Neural Networks (SNNs) that are specifically oriented towards data science applications. We emphasize the availability of spiking neuron models and learning rules to more easily direct decisions on the most suitable frameworks to carry out different types of research. Furthermore, we present an extension to the SpykeTorch framework that gives users access to a much broader choice of neuron models to embed in SNNs and make the code publicly available.

Related papers

• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence [51.6943465041708]
  Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency. We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips.
  arXiv  Detail & Related papers  (2023-10-25T13:15:17Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems [50.101188703826686]
  We present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of researchers across industry and academia.
  arXiv  Detail & Related papers  (2023-04-10T15:12:09Z)
• An overview of open source Deep Learning-based libraries for Neuroscience [0.0]
  This paper summarizes the main developments in Deep Learning and their relevance to Neuroscience. It then reviews neuroinformatic toolboxes and libraries, collected from the literature and from specific hubs of software projects oriented to neuroscience research.
  arXiv  Detail & Related papers  (2022-12-19T09:09:40Z)
• SPAIC: A Spike-based Artificial Intelligence Computing Framework [22.133585707508963]
  We present a Python based spiking neural network (SNN) simulation and training framework, aka SPAIC. It aims to support brain-inspired model and algorithm researches integrated with features from both deep learning and neuroscience. We provide a range of examples including neural circuits, deep SNN learning and neuromorphic applications.
  arXiv  Detail & Related papers  (2022-07-26T08:57:42Z)
• Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review [0.19573380763700707]
  We highlight challenges and opportunities that the future holds in the area of system software technology for neuromorphic computing. We provide a comprehensive overview of such frameworks proposed for both, platform-based design and hardware-software co-design.
  arXiv  Detail & Related papers  (2022-02-17T21:00:59Z)
• Neural Architecture Search for Dense Prediction Tasks in Computer Vision [74.9839082859151]
  Deep learning has led to a rising demand for neural network architecture engineering. neural architecture search (NAS) aims at automatically designing neural network architectures in a data-driven manner rather than manually. NAS has become applicable to a much wider range of problems in computer vision.
  arXiv  Detail & Related papers  (2022-02-15T08:06:50Z)
• NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks [3.9121275263540087]
  We present NeuroXplorer, a framework that is based on a generalized template for modeling a neuromorphic architecture. NeuroXplorer can perform both low-level cycle-accurate architectural simulations and high-level analysis with data-flow abstractions. We demonstrate the architectural exploration capabilities of NeuroXplorer through case studies with many state-of-the-art machine learning models.
  arXiv  Detail & Related papers  (2021-05-04T23:31:11Z)
• Neuromorphic Processing and Sensing: Evolutionary Progression of AI to Spiking [0.0]
  Spiking Neural Network algorithms hold the promise to implement advanced artificial intelligence using a fraction of the computations and power requirements. This paper explains the theoretical workings of neuromorphic technologies based on spikes, and overviews the state-of-art in hardware processors, software platforms and neuromorphic sensing devices. A progression path is paved for current machine learning specialists to update their skillset, as well as classification or predictive models from the current generation of deep neural networks to SNNs.
  arXiv  Detail & Related papers  (2020-07-10T20:54:42Z)
• The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding [97.85957811603251]
  We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks. A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm.
  arXiv  Detail & Related papers  (2020-02-19T03:05:28Z)

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.