1 citations · 1 across the 5 of their papers we have counts for
5 papers
DiffLUT-Net: Differentiable Training of FPGA LUT Networks with Learnable Connectivity
Jiaqi Ye, Xinrui Gong, Jingcun Wang +3
Field-programmable gate arrays (FPGAs) enable efficient neural-network inference, but most deployment flows either accelerate multiply-accumulate operations or convert pretrained q…
CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Context LLM Inference
Xiaolin Lin, Jingcun Wang, Olga Kondrateva +3
Long-context large language model (LLM) inference is increasingly constrained by the memory footprint and decoding cost of key-value (KV) caches, limiting sustainable deployment on…
CompressKV: Semantic Retrieval Heads Know What Tokens are Not Important Before Generation
Xiaolin Lin, Jingcun Wang, Olga Kondrateva +3
Recent advances in large language models (LLMs) have significantly boosted long-context processing. However, the increasing key-value (KV) cache size poses critical challenges to m…
Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression
Jingcun Wang, Yu-Guang Chen, Ing-Chao Lin +2
Large Language Models (LLMs) have achieved remarkable breakthroughs. However, the huge number of parameters in LLMs require significant amount of memory storage in inference, which…
Class-Aware Pruning for Efficient Neural Networks
Mengnan Jiang, Jingcun Wang, Amro Eldebiky +4
Deep neural networks (DNNs) have demonstrated remarkable success in various fields. However, the large number of floating-point operations (FLOPs) in DNNs poses challenges for thei…