Neuromorphic Hardware learns to learn

Abstract

Hyperparameters and learning algorithms for neuromorphic hardware are usually chosen by hand. In contrast, the hyperparameters and learning algorithms of networks of neurons in the brain, which they aim to emulate, have been optimized through extensive evolutionary and developmental processes for specific ranges of computing and learning tasks. Occasionally this process has been emulated through genetic algorithms, but these require themselves hand-design of their details and tend to provide a limited range of improvements. We employ instead other powerful gradient-free optimization tools, such as cross-entropy methods and evolutionary strategies, in order to port the function of biological optimization processes to neuromorphic hardware. As an example, we show that this method produces neuromorphic agents that learn very efficiently from rewards. In particular, meta-plasticity, i.e., the optimization of the learning rule which they use, substantially enhances reward-based learning capability of the hardware. In addition, we demonstrate for the first time Learning-to-Learn benefits from such hardware, in particular, the capability to extract abstract knowledge from prior learning experiences that speeds up the learning of new but related tasks. Learning-to-Learn is especially suited for accelerated neuromorphic hardware, since it makes it feasible to carry out the required very large number of network computations.

References in corpus (7)

• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning
• Learning to reinforcement learn
• A Survey of Neuromorphic Computing and Neural Networks in Hardware
• Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
• An Accelerated LIF Neuronal Network Array for a Large Scale Mixed-Signal Neuromorphic Architecture
• Transfer Learning for Speech and Language Processing

Cited by in corpus (8)

• Adaptive Extreme Edge Computing for Wearable Devices
• Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
• Reservoirs learn to learn
• Extending BrainScaleS OS for BrainScaleS-2
• Ultra-Low Power Neuromorphic Obstacle Detection Using a Two-Dimensional Materials-Based Subthreshold Transistor
• Evolving to learn: discovering interpretable plasticity rules for spiking networks
• Bridge Networks: Relating Inputs through Vector-Symbolic Manipulations
• 2022 Roadmap on Neuromorphic Computing and Engineering