An Online Learning Algorithm for Neuromorphic Hardware Implementation
This work addresses efficient hardware implementation for neuromorphic computing, but it appears incremental as it adapts existing principles to a specific framework.
The authors tackled the problem of online learning for neuromorphic hardware by proposing a sign-based online learning (SOL) algorithm for the Trainable Analogue Block (TAB) framework, showing it can train TAB for various regression tasks.
We propose a sign-based online learning (SOL) algorithm for a neuromorphic hardware framework called Trainable Analogue Block (TAB). The TAB framework utilises the principles of neural population coding, implying that it encodes the input stimulus using a large pool of nonlinear neurons. The SOL algorithm is a simple weight update rule that employs the sign of the hidden layer activation and the sign of the output error, which is the difference between the target output and the predicted output. The SOL algorithm is easily implementable in hardware, and can be used in any artificial neural network framework that learns weights by minimising a convex cost function. We show that the TAB framework can be trained for various regression tasks using the SOL algorithm.