Abstract: If we had a dollar for every time we heard “It will never scale!”, then neuromorphic engineers would be billionaires. This presentation will be centered on SpikeGPT, the first large-scale language model (LLM) using spiking neural nets (SNNs), and possibly the largest SNN that has been trained using error backpropagation.

The need for lightweight language models is more pressing than ever, especially now that we are becoming increasingly reliant on them from word processors and search engines, to code troubleshooting and academic grant writing. Our dependence on a single LLM means that every user is potentially pooling sensitive data into a singular database, which leads to significant security risks if breached.

SpikeGPT was built to move towards addressing the privacy and energy consumption challenges we presently run into using Transformer blocks. Our approach decomposes self-attention down into a recurrent form that is compatible with spiking neurons, along with dynamical weight matrices where the dynamics are learnable, rather than the parameters as with conventional deep learning.

We will provide an overview of what SpikeGPT does, how it works, and what it took to train it successfully. We will also provide a demo on how users can download pre-trained models available on HuggingFace so that listeners are able to experiment with them.

Link to the talk can be found here.