1 citations · 1 across the 7 of their papers we have counts for
7 papers
PADE: A Predictor-Free Sparse Attention Accelerator via Unified Execution and Stage Fusion
Huizheng Wang, Hongbin Wang, Zichuan Wang +5
Attention-based models have revolutionized AI, but the quadratic cost of self-attention incurs severe computational and memory overhead. Sparse attention methods alleviate this by…
From Quarter to All: Accelerating Speculative LLM Decoding via Floating-Point Exponent Remapping and Parameter Sharing
Yushu Zhao, Yubin Qin, Yang Wang +5
Large language models achieve impressive performance across diverse tasks but exhibit high inference latency due to their large parameter sizes. While quantization reduces model si…
MoBiLE: Efficient Mixture-of-Experts Inference on Consumer GPU with Mixture of Big Little Experts
Yushu Zhao, Yubin Qin, Yang Wang +5
Mixture-of-Experts (MoE) models have recently demonstrated exceptional performance across a diverse range of applications. The principle of sparse activation in MoE models facilita…
MCBP: A Memory-Compute Efficient LLM Inference Accelerator Leveraging Bit-Slice-enabled Sparsity and Repetitiveness
Huizheng Wang, Zichuan Wang, Zhiheng Yue +8
Large language models (LLMs) face significant inference latency due to inefficiencies in GEMM operations, weight access, and KV cache access, especially in real-time scenarios. Thi…
SOFA: A Compute-Memory Optimized Sparsity Accelerator via Cross-Stage Coordinated Tiling
Huizheng Wang, Jiahao Fang, Xinru Tang +9
Benefiting from the self-attention mechanism, Transformer models have attained impressive contextual comprehension capabilities for lengthy texts. The requirements of high-throughp…
HQNAS: Auto CNN deployment framework for joint quantization and architecture search
Hongjiang Chen, Yang Wang, Leibo Liu +2
Deep learning applications are being transferred from the cloud to edge with the rapid development of embedded computing systems. In order to achieve higher energy efficiency with…
FAQS: Communication-efficient Federate DNN Architecture and Quantization Co-Search for personalized Hardware-aware Preferences
Hongjiang Chen, Yang Wang, Leibo Liu +2
Due to user privacy and regulatory restrictions, federate learning (FL) is proposed as a distributed learning framework for training deep neural networks (DNN) on decentralized dat…