2 citations · 4 across the 11 of their papers we have counts for
13 papers
Learnable Low-Rank Polynomial Sketch for Effective Linear Attention
Zongyue Qin, Haoran Deng, Jason Cong +1
Softmax attention in Transformers suffers from quadratic complexity in sequence length, making it impractical for long-context applications. Linear attention alleviates this issue…
UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale
Hanyu Li, Yi-Ping Hsu, Aditya Mantha +17
Modern recommendation systems predominantly train retrieval and ranking as separate models despite both increasingly relying on large transformers encoding the same user behavior d…
ConFu: Contemplate the Future for Better Speculative Sampling
Zongyue Qin, Raghavv Goel, Mukul Gagrani +3
Speculative decoding has emerged as a powerful approach to accelerate large language model (LLM) inference by employing lightweight draft models to propose candidate tokens that ar…
STree: Speculative Tree Decoding for Hybrid State-Space Models
Yangchao Wu, Zongyue Qin, Alex Wong +1
Speculative decoding is a technique to leverage hardware concurrency in order to enable multiple steps of token generation in a single forward pass, thus improving the efficiency o…
Heuristic Methods are Good Teachers to Distill MLPs for Graph Link Prediction
Zongyue Qin, Shichang Zhang, Mingxuan Ju +3
Link prediction is a crucial graph-learning task with applications including citation prediction and product recommendation. Distilling Graph Neural Networks (GNNs) teachers into M…
Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis
Weikai Li, Ding Wang, Zijian Ding +4
High-level synthesis (HLS) is a widely used tool in designing Field Programmable Gate Array (FPGA). HLS enables FPGA design with software programming languages by compiling the sou…
Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference
Zongyue Qin, Zifan He, Neha Prakriya +2
Large language models (LLMs) have shown outstanding performance across numerous real-world tasks. However, the autoregressive nature of these models makes the inference process slo…
Efficient Task Transfer for HLS DSE
Zijian Ding, Atefeh Sohrabizadeh, Weikai Li +3
There have been several recent works proposed to utilize model-based optimization methods to improve the productivity of using high-level synthesis (HLS) to design domain-specific…
Relational Database Augmented Large Language Model
Zongyue Qin, Chen Luo, Zhengyang Wang +2
Large language models (LLMs) excel in many natural language processing (NLP) tasks. However, since LLMs can only incorporate new knowledge through training or supervised fine-tunin…
Cross-Modality Program Representation Learning for Electronic Design Automation with High-Level Synthesis
Zongyue Qin, Yunsheng Bai, Atefeh Sohrabizadeh +4
In recent years, domain-specific accelerators (DSAs) have gained popularity for applications such as deep learning and autonomous driving. To facilitate DSA designs, programmers us…
Optimized Multi-Token Joint Decoding with Auxiliary Model for LLM Inference
Zongyue Qin, Ziniu Hu, Zifan He +3
Large language models (LLMs) have achieved remarkable success across diverse tasks, yet their inference processes are hindered by substantial time and energy demands due to single-…
HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing
Zifan He, Yingqi Cao, Zongyue Qin +3
Transformer-based large language models (LLM) have been widely used in language processing applications. However, due to the memory constraints of the devices, most of them restric…
ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation
Yunsheng Bai, Atefeh Sohrabizadeh, Zongyue Qin +3
Recent years have witnessed the growing popularity of domain-specific accelerators (DSAs), such as Google's TPUs, for accelerating various applications such as deep learning, searc…