1 citations · 1 across the 10 of their papers we have counts for
11 papers
PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition
Ziyan Gan, Fangxin Liu, Chenyang Guan +10
Mixture-of-Experts (MoE) architectures scale Large Language Model (LLM) capacity efficiently by activating a sparse subset of experts per token. However, modern MoE inference remai…
GEMM-GS: Accelerating 3D Gaussian Splatting on Tensor Cores with GEMM-Compatible Blending
Haomin Li, Bowen Zhu, Fangxin Liu +4
Neural Radiance Fields (NeRF) enables 3D scene reconstruction from several 2D images but incurs high rendering latency via its point-sampling design. 3D Gaussian Splatting (3DGS) i…
LaMoS: Enabling Efficient Large Number Modular Multiplication through SRAM-based CiM Acceleration
Haomin Li, Fangxin Liu, Chenyang Guan +3
Barrett's algorithm is one of the most widely used methods for performing modular multiplication, a critical nonlinear operation in modern privacy computing techniques such as homo…
SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization
Zhixiong Zhao, Fangxin Liu, Junjie Wang +4
The emergence of accurate open large language models (LLMs) has sparked a push for advanced quantization techniques to enable efficient deployment on end-user devices. In this pape…
QUARK: Quantization-Enabled Circuit Sharing for Transformer Acceleration by Exploiting Common Patterns in Nonlinear Operations
Zhixiong Zhao, Haomin Li, Fangxin Liu +5
Transformer-based models have revolutionized computer vision (CV) and natural language processing (NLP) by achieving state-of-the-art performance across a range of benchmarks. Howe…
ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering
Fangxin Liu, Haomin Li, Bowen Zhu +4
Neural Radiance Fields (NeRF) offer significant promise for generating photorealistic images and videos. However, existing mainstream neural rendering models often fall short in me…
FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization
Fangxin Liu, Zongwu Wang, JinHong Xia +6
The rapid advancement of large language models (LLMs) has exacerbated the memory bottleneck due to the widening gap between model parameter scaling and hardware capabilities. While…
MILLION: Mastering Long-Context LLM Inference Via Outlier-Immunized KV Product Quantization
Zongwu Wang, Peng Xu, Fangxin Liu +7
Large language models (LLMs) are increasingly utilized for complex tasks requiring longer context lengths, with some models supporting up to 128K or 1M tokens. This trend, however,…
ALLMod: Exploring rea-Efficiency of UT-based arge Number ular Reduction via Hybrid Workloads
Fangxin Liu, Haomin Li, Zongwu Wang +5
Modular arithmetic, particularly modular reduction, is widely used in cryptographic applications such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). High-bit-width…
TokenRing: An Efficient Parallelism Framework for Infinite-Context LLMs via Bidirectional Communication
Zongwu Wang, Fangxin Liu, Mingshuai Li +1
Efficient parallelization of Large Language Models (LLMs) with long sequences is essential but challenging due to their significant computational and memory demands, particularly s…
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network
Fangxin Liu, Wenbo Zhao, Yilong Zhao +6
Resistive Random-Access-Memory (ReRAM) crossbar is a promising technique for deep neural network (DNN) accelerators, thanks to its in-memory and in-situ analog computing abilities…