Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work and Background


Analysis
3.1 Limitations about Existing ReLUficatio
3.2 dReLU


Are Neurons in Expert still Sparsely Activated?


dReLU Sparsification


Experiments Results
6.1 Downstream Tasks Performance
6.2 Sparsity of Sparsified Models


Practical Inference Speedup Evaluation
7.1 Experiments Setting
7.2 Pure CPU Inference and 7.3 Hybrid GPU-CPU Inference
7.4 Deploy LLMs on mobile phones


Conclusion and References Related Work and Background Related Work and Background Related Work and Background Analysis
3.1 Limitations about Existing ReLUficatio
3.2 dReLU Analysis 3.1 Limitations about Existing ReLUficatio 3.1 Limitations about Existing ReLUficatio 3.2 dReLU 3.2 dReLU Are Neurons in Expert still Sparsely Activated? Are Neurons in Expert still Sparsely Activated? Are Neurons in Expert still Sparsely Activated? dReLU Sparsification dReLU Sparsification dReLU Sparsification Experiments Results
6.1 Downstream Tasks Performance
6.2 Sparsity of Sparsified Models Experiments Results 6.1 Downstream Tasks Performance 6.1 Downstream Tasks Performance 6.2 Sparsity of Sparsified Models 6.2 Sparsity of Sparsified Models Practical Inference Speedup Evaluation
7.1 Experiments Setting
7.2 Pure CPU Inference and 7.3 Hybrid GPU-CPU Inference
7.4 Deploy LLMs on mobile phones Practical Inference Speedup Evaluation Practical Inference Speedup Evaluation 7.1 Experiments Setting 7.1 Experiments Setting 7.2 Pure CPU Inference and 7.3 Hybrid GPU-CPU Inference 7.2 Pure CPU Inference and 7.3 Hybrid GPU-CPU Inference 7.4 Deploy LLMs on mobile phones 7.4 Deploy LLMs on mobile phones Conclusion and References Conclusion and References Conclusion and References A. Appendix / supplemental material A. Appendix / supplemental material B. Limitation B. Limitation C. Broader Impact C. Broader Impact 8 Conclusion We propose a novel dReLU-based sparsification method that increases model sparsity to 90% while maintaining performance, achieving a 2-5× speedup in inference. This method significantly reduces resource requirements for deploying large models, making them more environmentally friendly and accessible. 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(1) Yixin Song, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University;
(2) Haotong Xie, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University;
(3) Zhengyan Zhang, Department of Computer Science and Technology, Tsinghua University;
(4) Bo Wen, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University;
(5) Li Ma, Shanghai Artificial Intelligence Laboratory;
(6) Zeyu Mi, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University Mi yzmizeyu@sjtu.edu.cn);
(7) Haibo Chen, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University. Authors: Authors: (1) Yixin Song, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University; (2) Haotong Xie, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University; (3) Zhengyan Zhang, Department of Computer Science and Technology, Tsinghua University; (4) Bo Wen, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University; (5) Li Ma, Shanghai Artificial Intelligence Laboratory; (6) Zeyu Mi, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University Mi yzmizeyu@sjtu.edu.cn); (7) Haibo Chen, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University. This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

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dReLU Sparsification: High-Performance 90% Sparsity for Next-Gen LLMs

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