140 citations · 144 across the 6 of their papers we have counts for
6 papers
Towards Accurate and Efficient Sub-8-Bit Integer Training
Wenjin Guo, Donglai Liu, Weiying Xie +7
Neural network training is a memory- and compute-intensive task. Quantization, which enables low-bitwidth formats in training, can significantly mitigate the workload. To reduce qu…
Efficient and Effective Retrieval of Dense-Sparse Hybrid Vectors using Graph-based Approximate Nearest Neighbor Search
Haoyu Zhang, Jun Liu, Zhenhua Zhu +5
ANNS for embedded vector representations of texts is commonly used in information retrieval, with two important information representations being sparse and dense vectors. While it…
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs
Shulin Zeng, Jun Liu, Guohao Dai +14
Transformer-based Large Language Models (LLMs) have made a significant impact on various domains. However, LLMs' efficiency suffers from both heavy computation and memory overheads…
Exploring the Potential of Low-bit Training of Convolutional Neural Networks
Kai Zhong, Xuefei Ning, Guohao Dai +5
In this work, we propose a low-bit training framework for convolutional neural networks, which is built around a novel multi-level scaling (MLS) tensor format. Our framework focuse…
Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud
Shulin Zeng, Guohao Dai, Hanbo Sun +5
FPGAs have shown great potential in providing low-latency and energy-efficient solutions for deep neural network (DNN) inference applications. Currently, the majority of FPGA-based…
A Survey of FPGA-Based Neural Network Accelerator
Kaiyuan Guo, Shulin Zeng, Jincheng Yu +2
Recent researches on neural network have shown significant advantage in machine learning over traditional algorithms based on handcrafted features and models. Neural network is now…