20 citations · 47 across the 7 of their papers we have counts for
8 papers
PipeSwift: Revisiting Pipeline Parallelism for Large-Scale Completion-Oriented Agentic LLM Serving
Shiju Wang, Fei Ren, Fangcheng Fu +4
LLM agents execute long-horizon workflows where each model response determines the progress of subsequent tool interactions and environment transitions. Unlike chatbot serving, whe…
ES-dLLM: Efficient Inference for Diffusion Large Language Models by Early-Skipping
Zijian Zhu, Fei Ren, Zhanhong Tan +1
Diffusion large language models (dLLMs) are emerging as a promising alternative to autoregressive models (ARMs) due to their ability to capture bidirectional context and the potent…
Cocco: Hardware-Mapping Co-Exploration towards Memory Capacity-Communication Optimization
Zhanhong Tan, Zijian Zhu, Kaisheng Ma
Memory is a critical design consideration in current data-intensive DNN accelerators, as it profoundly determines energy consumption, bandwidth requirements, and area costs. As DNN…
Gemini: Mapping and Architecture Co-exploration for Large-scale DNN Chiplet Accelerators
Jingwei Cai, Zuotong Wu, Sen Peng +5
Chiplet technology enables the integration of an increasing number of transistors on a single accelerator with higher yield in the post-Moore era, addressing the immense computatio…
YOLoC: DeploY Large-Scale Neural Network by ROM-based Computing-in-Memory using ResiduaL Branch on a Chip
Yiming Chen, Guodong Yin, Zhanhong Tan +6
Computing-in-memory (CiM) is a promising technique to achieve high energy efficiency in data-intensive matrix-vector multiplication (MVM) by relieving the memory bottleneck. Unfort…
Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks
Runpei Dong, Zhanhong Tan, Mengdi Wu +2
Quantized neural networks typically require smaller memory footprints and lower computation complexity, which is crucial for efficient deployment. However, quantization inevitably…
PCNN: Pattern-based Fine-Grained Regular Pruning towards Optimizing CNN Accelerators
Zhanhong Tan, Jiebo Song, Xiaolong Ma +8
Weight pruning is a powerful technique to realize model compression. We propose PCNN, a fine-grained regular 1D pruning method. A novel index format called Sparsity Pattern Mask (S…
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models
Linfeng Zhang, Zhanhong Tan, Jiebo Song +3
Remarkable achievements have been attained by deep neural networks in various applications. However, the increasing depth and width of such models also lead to explosive growth in…