4 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2025
EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models
Xingrun Xing, Zheng Liu, Shitao Xiao +6
Modern large language models (LLMs) driven by scaling laws, achieve intelligence emergency in large model sizes. Recently, the increasing concerns about cloud costs, latency, and p…
cs.LG2024★ 4 cited
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
Xingrun Xing, Boyan Gao, Zheng Zhang +5
Recent advancements in large language models (LLMs) with billions of parameters have improved performance in various applications, but their inference processes demand significant…
cs.NE2024★ 1 cited
SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms
Xingrun Xing, Zheng Zhang, Ziyi Ni +6
Towards energy-efficient artificial intelligence similar to the human brain, the bio-inspired spiking neural networks (SNNs) have advantages of biological plausibility, event-drive…