23 citations · 41 across the 8 of their papers we have counts for
10 papers
ActTune: Action-Aware Precision and GPU Operating-Point Adaptation for Energy-Efficient Vision-Language-Action Inference
Zou Qingyun, Bin Gao, Wenju Zhao +3
Vision-language-action (VLA) policies repeatedly invoke inference to control robots, making graphics processing unit (GPU) energy a recurring cost of task execution. Reducing energ…
ECO: Energy-Oriented Configuration Optimization for Attention FFN Disaggregated LLM Serving
Zou Qingyun, Bin Gao, Zhuobin Huang +3
Energy-efficient LLM serving requires minimizing serving GPU energy while meeting latency and throughput service-level objectives (SLOs). Attention--FFN disaggregation (AFD) enable…
Reward-Weighted On-Policy Distillation with an Open Property-Equivalence Verifier for NL-to-SVA Generation
Qingyun Zou, Yingze Li, Tianen Liu +2
LLM-based generation of SystemVerilog Assertions (SVA) is often reported as nearing saturation, with the strongest specialized model reaching accuracy on NL2SVA-Human.…
HLS-Seek: QoR-Aware Code Generation for High-Level Synthesis via Proxy Comparative Reward Reinforcement Learning
Qingyun Zou, Feng Yu, Hongshi Tan +3
High-Level Synthesis (HLS) compiles algorithmic C/C++ descriptions into hardware, with Quality of Results (QoR)---latency and resource utilization---critically governed by pragma c…
RidgeWalker: Perfectly Pipelined Graph Random Walks on FPGAs
Hongshi Tan, Yao Chen, Xinyu Chen +4
Graph Random Walks (GRWs) offer efficient approximations of key graph properties and have been widely adopted in many applications. However, GRW workloads are notoriously difficult…
Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems
Benjamin Chen Ming Choong, Tao Luo, Cheng Liu +3
Deep neural networks generate and process large volumes of data, posing challenges for low-resource embedded systems. In-memory computing has been demonstrated as an efficient comp…
HongTu: Scalable Full-Graph GNN Training on Multiple GPUs (via communication-optimized CPU data offloading)
Qiange Wang, Yao Chen, Weng-Fai Wong +1
Full-graph training on graph neural networks (GNN) has emerged as a promising training method for its effectiveness. Full-graph training requires extensive memory and computation r…
LightRW: FPGA Accelerated Graph Dynamic Random Walks
Hongshi Tan, Xinyu Chen, Yao Chen +2
Graph dynamic random walks (GDRWs) have recently emerged as a powerful paradigm for graph analytics and learning applications, including graph embedding and graph neural networks.…
ReGraph: Scaling Graph Processing on HBM-enabled FPGAs with Heterogeneous Pipelines
Xinyu Chen, Yao Chen, Feng Cheng +3
The use of FPGAs for efficient graph processing has attracted significant interest. Recent memory subsystem upgrades including the introduction of HBM in FPGAs promise to further a…
ThundeRiNG: Generating Multiple Independent Random Number Sequences on FPGAs
Hongshi Tan, Xinyu Chen, Yao Chen +2
In this paper, we propose ThundeRiNG, a resource-efficient and high-throughput system for generating multiple independent sequences of random numbers (MISRN) on FPGAs. Generating M…