5 papers
Towards Building Non-Fine-Tunable Foundation Models
Ziyao Wang, Nizhang Li, Pingzhi Li +3
Open-sourcing foundation models (FMs) enables broad reuse but also exposes model trainers to economic and safety risks from unrestricted downstream fine-tuning. We address this pro…
AsyncSpade: Efficient Test-Time Scaling with Asynchronous Sparse Decoding
Shuqing Luo, Yilin Guan, Pingzhi Li +2
Test-time scaling (TTS) boosts LLM reasoning via long chain-of-thought (CoT), but the linear KV-cache growth amplifies the memory-bound bottleneck of LLM decoding. Query-aware page…
Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference
Shuqing Luo, Pingzhi Li, Jie Peng +7
Mixture-of-experts (MoE) architectures could achieve impressive computational efficiency with expert parallelism, which relies heavily on all-to-all communication across devices. U…
H3PIMAP: A Heterogeneity-Aware Multi-Objective DNN Mapping Framework on Electronic-Photonic Processing-in-Memory Architectures
Ziang Yin, Aashish Poonia, Ashish Reddy Bommana +7
The future of artificial intelligence (AI) acceleration demands a paradigm shift beyond the limitations of purely electronic or photonic architectures. Photonic analog computing de…
GroverGPT: A Large Language Model with 8 Billion Parameters for Quantum Searching
Haoran Wang, Pingzhi Li, Min Chen +3
Quantum computing is an exciting non-Von Neumann paradigm, offering provable speedups over classical computing for specific problems. However, the practical limits of classical sim…