Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks
This work addresses the problem of energy-efficient AI on neuromorphic hardware for applications like speech and digit recognition, representing an incremental improvement by extending Eventprop with loss shaping and optimizations.
The researchers tackled the challenge of scaling Eventprop for gradient descent in spiking neural networks to achieve state-of-the-art performance on keyword recognition benchmarks, resulting in a 3X faster and 4X less memory-intensive implementation compared to a leading surrogate-gradient method.
Event-based machine learning promises more energy-efficient AI on future neuromorphic hardware. Here, we investigate how the recently discovered Eventprop algorithm for gradient descent on exact gradients in spiking neural networks can be scaled up to challenging keyword recognition benchmarks. We implemented Eventprop in the GPU-enhanced Neural Networks framework and used it for training recurrent spiking neural networks on the Spiking Heidelberg Digits and Spiking Speech Commands datasets. We found that learning depended strongly on the loss function and extended Eventprop to a wider class of loss functions to enable effective training. We then tested a large number of data augmentations and regularisations as well as exploring different network structures; and heterogeneous and trainable timescales. We found that when combined with two specific augmentations, the right regularisation and a delay line input, Eventprop networks with one recurrent layer achieved state-of-the-art performance on Spiking Heidelberg Digits and good accuracy on Spiking Speech Commands. In comparison to a leading surrogate-gradient-based SNN training method, our GeNN Eventprop implementation is 3X faster and uses 4X less memory. This work is a significant step towards a low-power neuromorphic alternative to current machine learning paradigms.