Spiking neural networks follow similar scaling rules as dense language models
Compute-Optimal Scaling Laws for Spiking Language Models
Machine LearningComputation and LanguageNeural and Evolutionary Computing
Summary
Large language models can be made more energy efficient using spiking neural networks, which use sparse, binary signals. The authors studied how to best scale these spiking models with computing power and found that the relationship between model size and compute remains similar to traditional models. They also quantified the accuracy cost of this efficiency approach, showing part of the loss comes from the model type itself and part from spiking. This helps guide how to build spiking language models while understanding the tradeoffs involved.
What this means in practice
- •For machine learning engineers: Optimize training and scaling decisions for spiking language models to achieve energy-efficient performance under limited compute resources.
- •For embedded ai developers: Design language models for low-power devices by balancing accuracy and efficiency using the study’s compute-optimal scaling guidance.
Authors
Dan Saunders, Benjamin Ren, Catherine He
Abstract
Spiking neural networks target energy-efficient language modeling through sparse, binary activations, but the efficiency-accuracy tradeoff of spiking language models is uncharacterized at scale. We present the first Chinchilla-style IsoFLOP scaling study of a spiking language model, sweeping SpikeGPT-family models (RWKV backbone) from width 384 to 1536 across compute budgets from $3\times10^{17}$ to $10^{19}$ FLOPs on FineWeb-EDU. We report two findings. First, the compute-optimal frontier is $N_{opt} \propto C^{0.56}$, close to the 0.50 that Hoffmann et al. report for dense Transformers, so spiking does not change how parameters and data should be traded off under a compute budget. Second, against non-spiking RWKV and Transformer baselines trained the same way, the loss gap splits into a backbone cost of about 0.16 nats that does not change with compute and a spiking cost that falls from 0.34 to about 0.23 nats and then levels off. These results give the first compute-optimal recipe for spiking language models and quantify the accuracy cost of their sparsity.