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Xiaotian Song

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3 papers

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AAAI Conference 2026 Conference Paper

LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models

Long Chen
Xiaotian Song
Yanan Sun

Spiking Large Language Models (LLMs) have emerged as an energy-efficient alternative to conventional LLMs through their event-driven computation. To effectively obtain spiking LLMs, researchers develop different ANN-to-SNN conversion methods by leveraging pre-trained ANN parameters while inheriting the energy efficiency of SNN. However, existing conversion methods struggle with extreme activation outliers and incompatible nonlinear operations of ANN-based LLMs. To address this, we propose a loss-less ANN-SNN conversion for fully spike-driven LLMs, termed LAS. Specifically, LAS introduces two novel neurons to convert the activation outlier and nonlinear operation of ANN-based LLMs. Moreover, LAS tailors the spike-equivalent Transformer components for spiking LLMs, which can ensure full spiking conversion without any loss of performance. Experimental results on six language models and two vision-language models demonstrate that LAS achieves loss-less conversion. Notably, on OPT-66B, LAS even improves the accuracy of 2% on the WSC task. In addition, the parameter and ablation studies further verify the effectiveness of LAS.

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IJCAI Conference 2024 Conference Paper

One-step Spiking Transformer with a Linear Complexity

Xiaotian Song
Andy Song
Rong Xiao
Yanan Sun

Spiking transformers have recently emerged as a robust alternative in deep learning. One focus of this field is the reduction of energy consumption, given that spiking transformers require lengthy simulation timesteps and complex floating-point attention mechanisms. In this paper, we propose a one-step approach that requires only one timestep and is of linear complexity. The proposed One-step Spiking Transformer (OST) incorporates a Time Domain Compression and Compensation (TDCC) component, which can significantly mitigate the spatio-temporal overhead of spiking transformers. Another novel component in OST is the Spiking Linear Transformation (SLT), designed to greatly reduce the number of floating-point multiply-and-accumulate operations. Experiments on both static and neuromorphic images show that OST can perform as well as or better than SOTA methods with just one timestep, even for more difficult tasks. For instance, comparing with Spikeformer, OST gains 1. 59% in accuracy on ImageNet, yet 40. 27% more efficient, and gains 0. 7% on DVS128 Gesture. The supplementary materials and source code are available at https: //github. com/songxt3/OST.

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IJCAI Conference 2024 Conference Paper

Revisiting Neural Networks for Continual Learning: An Architectural Perspective

Aojun Lu
Tao Feng
Hangjie Yuan
Xiaotian Song
Yanan Sun

Efforts to overcome catastrophic forgetting have primarily centered around developing more effective Continual Learning (CL) methods. In contrast, less attention was devoted to analyzing the role of network architecture design (e. g. , network depth, width, and components) in contributing to CL. This paper seeks to bridge this gap between network architecture design and CL, and to present a holistic study on the impact of network architectures on CL. This work considers architecture design at the network scaling level, i. e. , width and depth, and also at the network components, i. e. , skip connections, global pooling layers, and down-sampling. In both cases, we first derive insights through systematically exploring how architectural designs affect CL. Then, grounded in these insights, we craft a specialized search space for CL and further propose a simple yet effective ArchCraft method to steer a CL-friendly architecture, namely, this method recrafts AlexNet/ResNet into AlexAC/ResAC. Experimental validation across various CL settings and scenarios demonstrates that improved architectures are parameter-efficient, achieving state-of-the-art performance of CL while being 86%, 61%, and 97% more compact in terms of parameters than the naive CL architecture in Task IL and Class IL. Code is available at https: //github. com/byyx666/ArchCraft.

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