These September 2025 papers present a technical report on SpikingBrain, a novel family of large language models (LLMs) that draw inspiration from brain mechanisms to address the efficiency challenges of traditional Transformer architectures. The research focuses on efficient long-context training and inference by developing hybrid linear attention architectures and an adaptive threshold spiking neuron scheme. A significant aspect of this work is the successful training and deployment of these models on non-NVIDIA GPU clusters, specifically the MetaX platform, demonstrating the feasibility of large-scale LLM development on alternative hardware. The authors highlight substantial speedups in inference for long sequences and significant reductions in energy consumption through the sparse, event-driven spiking design, with performance comparable to established Transformer baselines while using considerably less training data. This work ultimately aims to advance the design of energy-efficient and scalable brain-inspired LLMs for next-generation computing systems, including neuromorphic hardware.
Sources:
https://arxiv.org/pdf/2509.05276
https://arxiv.org/html/2509.05276v1
