4 citations · 8 across the 14 of their papers we have counts for
10 papers · 1 filter
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…
PUSHtap: PIM-based In-Memory HTAP with Unified Data Storage Format
Yilong Zhao, Mingyu Gao, Huanchen Zhang +5
Hybrid transaction/analytical processing (HTAP) is an emerging database paradigm that supports both online transaction processing (OLTP) and online analytical processing (OLAP) wor…
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…