Introduction

Mainstream Transformers dominate LLM development, but their cost profile is daunting: training scales quadratically with sequence length and inference memory grows linearly. SpikingBrain introduces a brain-inspired alternative, combining linear and hybrid-linear attention, adaptive spiking neurons, and multi-scale sparsity to deliver efficient large models on non-NVIDIA clusters.

Core Concepts

Three pillars define SpikingBrain:

• Linear Attention: compresses context into linear state updates, avoiding O(n²) scaling.
• Spiking Neurons: event-driven units that fire discrete spikes, skipping computation when inactive.
• Mixture-of-Experts (MoE): sparse FFN layers where only a subset of experts is activated per token.

Architecture and Methodology

Two models were developed:

• SpikingBrain-7B: a purely linear model alternating Sliding Window Attention and linear attention, tuned for long-context efficiency.
• SpikingBrain-76B: a hybrid-linear MoE model combining full, local, and linear attention intra-layer.

Key innovation: adaptive-threshold spiking neurons convert continuous activations into integer spike counts, enabling sparse and event-driven processing.

A conversion-based pipeline allows reusing pre-trained Transformer checkpoints, requiring only ∼150B tokens—just 2% of the data usually needed for training from scratch.

Experimental Results

Highlights from benchmarks:

• SpikingBrain-7B recovers nearly 90% of Qwen2.5-7B’s performance, despite being fully linear.
• SpikingBrain-76B matches or outperforms models like Llama2-70B and Mixtral-8×7B.
• Achieves 100× TTFT speedup on 4M-token inputs.
• Delivers ~69% sparsity, cutting power consumption by up to 97% compared with FP16 MACs.

Practical Applications

Potential use cases include:

• Edge AI: a compressed 1B model deployed on CPUs showed up to 15× decoding speedup, enabling mobile and embedded inference.
• Cloud AI on alternative hardware: validated stable training on hundreds of MetaX GPUs.
• Neuromorphic hardware: event-driven spike coding aligns naturally with asynchronous architectures for ultra-low-power computing.

Limitations and Considerations

• The pure linear model still lags in raw accuracy compared to quadratic Transformers.
• Specialized operator libraries and frameworks are required, limiting portability.
• Ethical concerns: more efficient LLMs could accelerate unchecked proliferation without proper safeguards.

Future Directions

• Closer integration with neuromorphic chips to fully exploit event-driven computing.
• Improved spike coding strategies balancing accuracy and efficiency.
• Expanding the conversion pipeline to cover broader families of open-source models.

Conclusions

SpikingBrain demonstrates how brain-inspired design principles can drastically reduce training and inference costs while maintaining competitive performance. It marks a concrete step toward sustainable, scalable LLMs suitable for real-world deployment across edge and cloud environments.