Neuromorphic dreaming: A pathway to efficient learning in artificial agents

• URL: http://arxiv.org/abs/2405.15616v1
• Date: Fri, 24 May 2024 15:03:56 GMT
• Title: Neuromorphic dreaming: A pathway to efficient learning in artificial agents
• Authors: Ingo Blakowski, Dmitrii Zendrikov, Cristiano Capone, Giacomo Indiveri,
• Abstract summary: We present a hardware implementation of model-based reinforcement learning (MBRL) using spiking neural networks (SNNs) on mixed-signal analog/digital neuromorphic hardware. This approach leverages the energy efficiency of mixed-signal neuromorphic chips while achieving high sample efficiency. We validate the model by training the hardware implementation to play the Atari game Pong.
• Score: 2.6542148964152923
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI) computing platforms. Biological systems demonstrate remarkable abilities to learn complex skills quickly and efficiently. Inspired by this, we present a hardware implementation of model-based reinforcement learning (MBRL) using spiking neural networks (SNNs) on mixed-signal analog/digital neuromorphic hardware. This approach leverages the energy efficiency of mixed-signal neuromorphic chips while achieving high sample efficiency through an alternation of online learning, referred to as the "awake" phase, and offline learning, known as the "dreaming" phase. The model proposed includes two symbiotic networks: an agent network that learns by combining real and simulated experiences, and a learned world model network that generates the simulated experiences. We validate the model by training the hardware implementation to play the Atari game Pong. We start from a baseline consisting of an agent network learning without a world model and dreaming, which successfully learns to play the game. By incorporating dreaming, the number of required real game experiences are reduced significantly compared to the baseline. The networks are implemented using a mixed-signal neuromorphic processor, with the readout layers trained using a computer in-the-loop, while the other layers remain fixed. These results pave the way toward energy-efficient neuromorphic learning systems capable of rapid learning in real world applications and use-cases.

Related papers

• Training Hybrid Neural Networks with Multimode Optical Nonlinearities Using Digital Twins [2.8479179029634984]
  We introduce ultrashort pulse propagation in multimode fibers, which perform large-scale nonlinear transformations. Training the hybrid architecture is achieved through a neural model that differentiably approximates the optical system. Our experimental results achieve state-of-the-art image classification accuracies and simulation fidelity.
  arXiv  Detail & Related papers  (2025-01-14T10:35:18Z)
• Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing [3.735012564657653]
  Digital neuromorphic technology simulates the neural and synaptic processes of the brain using two stages of learning. We demonstrate our approach using event-driven vision sensor data and the Intel Loihi neuromorphic processor with its plasticity dynamics. Our methodology can be deployed with arbitrary plasticity models and can be applied to situations demanding quick learning and adaptation at the edge.
  arXiv  Detail & Related papers  (2024-08-28T13:51:52Z)
• Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks [0.0]
  We introduce and evaluate a brain-like neural network model capable of unsupervised representation learning. The model was tested on a diverse set of popular machine learning benchmarks.
  arXiv  Detail & Related papers  (2024-06-07T08:32:30Z)
• 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)
• An effective and efficient green federated learning method for one-layer neural networks [0.22499166814992436]
  Federated learning (FL) is one of the most active research lines in machine learning. We present a FL method, based on a neural network without hidden layers, capable of generating a global collaborative model in a single training round. We show that the method performs equally well in both identically and non-identically distributed scenarios.
  arXiv  Detail & Related papers  (2023-12-22T08:52:08Z)
• Continual learning autoencoder training for a particle-in-cell simulation via streaming [52.77024349608834]
  upcoming exascale era will provide a new generation of physics simulations with high resolution. These simulations will have a high resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. This work presents an approach that trains a neural network concurrently to a running simulation without data on a disk.
  arXiv  Detail & Related papers  (2022-11-09T09:55:14Z)
• SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving [0.0]
  Spiking Neural Networks are a new neural network design approach that promises tremendous improvements in power efficiency, computation efficiency, and processing latency. We first illustrate the applicability of spiking neural networks to a complex deep learning task namely Lidar based 3D object detection for automated driving.
  arXiv  Detail & Related papers  (2022-06-06T20:05:17Z)
• 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)
• Reservoir Memory Machines as Neural Computers [70.5993855765376]
  Differentiable neural computers extend artificial neural networks with an explicit memory without interference. We achieve some of the computational capabilities of differentiable neural computers with a model that can be trained very efficiently.
  arXiv  Detail & Related papers  (2020-09-14T12:01:30Z)
• The large learning rate phase of deep learning: the catapult mechanism [50.23041928811575]
  We present a class of neural networks with solvable training dynamics. We find good agreement between our model's predictions and training dynamics in realistic deep learning settings. We believe our results shed light on characteristics of models trained at different learning rates.
  arXiv  Detail & Related papers  (2020-03-04T17:52:48Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

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.