4 papers
SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting
Bang Hu, Changze Lv, Mingjie Li +3
Spiking neural networks (SNNs) offer an energy-efficient paradigm for time-series forecasting through spike-driven computation. However, recent SNN forecasters often pursue higher…
QuantaSpike: Short-Window Spike-Driven Quantization for Large Language Models
Bang Hu, Guowei Zhu, Changze Lv +4
Large language models (LLMs) achieve strong performance across many tasks but rely on dense multiply-accumulate (MAC) operations during inference, resulting in high energy cost. Sp…
Salca: A Sparsity-Aware Hardware Accelerator for Efficient Long-Context Attention Decoding
Wang Fan, Wei Cao, Xi Zha +5
Long contexts improve capabilities of large language models but pose serious hardware challenges: compute and memory footprints grow linearly with sequence length. Particularly, th…
SpikeSTAG: Spatial-Temporal Forecasting via GNN-SNN Collaboration
Bang Hu, Changze Lv, Mingjie Li +5
Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, offer a distinctive approach for capturing the complexities of temporal data. However, their…