15 citations · 15 across the 9 of their papers we have counts for
13 papers
ActKV: Efficient LLM Agents through Action-Guided KV Cache Management
Zihan Wang, Cheng Tang, Lei Gong +4
Agentic LLM inference accumulates long KV caches across iterative observation-reasoning-action loops, imposing substantial memory overhead and limiting serving throughput. Existing…
UnionSparse: An Index-Efficient Sparsity Framework for Low-Bit Sparse LLM Inference on Edge
Tianhao Jiang, Hang Gu, Teng Wang +9
Edge LLM inference combines sparsity and low-bit quantization to meet device memory, latency, and power limits. Yet quantization shrinks weight payloads without proportionally redu…
Realizable N:M Sparse Transformer Inference via Search-Kernel Co-Design
Yiming Liu, Wenqi Lou, Zhiguang Wang +4
Vision Transformers (ViTs) achieve strong accuracy but incur high inference latency. Semi-structured N:M sparsity can reduce arithmetic cost, yet its theoretical savings often fail…
Scheduling Cause-Effect Chains without Timing Anomalies in End-to-End Latency
Yixuan Zhu, Bo Zhang, Yinkang Gao +7
In real-time systems, both individual task execution and data propagation must meet strict timing constraints. Cause-effect (CE) chains are widely used to analyze such behaviors by…
Hermes: A Unified High-Performance NTT Architecture with Hybrid Dataflow
Hang Gu, Teng Wang, Qianyu Cheng +6
Fully Homomorphic Encryption (FHE) relies heavily on the Number Theoretic Transform (NTT), making NTT a major performance bottleneck due to its intensive polynomial computations. H…
Window-Diffusion: Accelerating Diffusion Language Model Inference with Windowed Token Pruning and Caching
Fengrui Zuo, Zhiwei Ke, Yiming Liu +3
Diffusion language models (DLMs) generate text through iterative denoising, but inference requires full-sequence attention at every iteration, resulting in substantial redundant co…
A Timing-Anomaly Free Dynamic Scheduling on Heterogeneous Systems
Yixuan Zhu, Yinkang Gao, Lei Gong +9
Heterogeneous systems commonly adopt dynamic scheduling algorithms to improve resource utilization and enhance scheduling flexibility. However, such flexibility may introduce timin…
Reducing End-to-End Latency of Cause-Effect Chains with Shared Cache Analysis
Yixuan Zhu, Yinkang Gao, Bo Zhang +8
Cause-effect chains, as a widely used modeling method in real-time embedded systems, are extensively applied in various safety-critical domains. End-to-end latency, as a key real-t…
Crystal-KV: Efficient KV Cache Management for Chain-of-Thought LLMs via Answer-First Principle
Zihan Wang, Cheng Tang, Lei Gong +5
Chain-of-Thought (CoT) reasoning in large language models (LLMs) significantly improves accuracy on complex tasks, yet incurs excessive memory overhead due to the long think-stage…
ActionFlow: A Pipelined Action Acceleration for Vision Language Models on Edge
Yuntao Dai, Hang Gu, Teng Wang +6
Vision-Language-Action (VLA) models have emerged as a unified paradigm for robotic perception and control, enabling emergent generalization and long-horizon task execution. However…
CoQMoE: Co-Designed Quantization and Computation Orchestration for Mixture-of-Experts Vision Transformer on FPGA
Jiale Dong, Hao Wu, Zihao Wang +5
Vision Transformers (ViTs) exhibit superior performance in computer vision tasks but face deployment challenges on resource-constrained devices due to high computational/memory dem…
UbiMoE: A Ubiquitous Mixture-of-Experts Vision Transformer Accelerator With Hybrid Computation Pattern on FPGA
Jiale Dong, Wenqi Lou, Zhendong Zheng +4
Compared to traditional Vision Transformers (ViT), Mixture-of-Experts Vision Transformers (MoE-ViT) are introduced to scale model size without a proportional increase in computatio…
Reconfigurable Hardware Accelerators: Opportunities, Trends, and Challenges
Chao Wang, Wenqi Lou, Lei Gong +5
With the emerging big data applications of Machine Learning, Speech Recognition, Artificial Intelligence, and DNA Sequencing in recent years, computer architecture research communi…