← Search

Zongyue Qin

7 accepted papers

2026

ConFu: Contemplate the Future for Better Speculative Sampling

ICML 2026poster

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 are subsequently verified by the target model. The effectiveness of this paradigm critically depends on the quality of the…

Cited by 0SourceScholar
2025

Dynamic-Width Speculative Beam Decoding for LLM Inference

AAAI 2025technical

Large language models (LLMs) based on transformer architecture have shown outstanding performance across numerous real-world tasks. However, the autoregressive nature of these models makes the inference process slow and costly. Speculative decoding has emerged as a promising solution, leveraging a…

2025

HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing

NAACL 2025long

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 restrict the context window. Even though recurrent models in previous works can memorize past tokens to enable unlimited contex…

2025

Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis

AAAI 2025technical

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 source code into an FPGA circuit. The source code includes a program (called ``kernel'') and several pragmas that instruct…

2025

Optimized Multi-Token Joint Decoding With Auxiliary Model for LLM Inference

ICLR 2025poster

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-token generation at each decoding step. While previous methods such as speculative decoding mitigate these inefficiencie…

Cited by 3SourcePDFScholar
2025

STree: Speculative Tree Decoding for Hybrid State Space Models

NeurIPS 2025poster

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 of large-scale autoregressive (AR) Transformer models. State-space models (SSMs) are already more efficient than AR Trans…

Cited by 0SourcecodeScholar
2023

Towards a Comprehensive Benchmark for High-Level Synthesis Targeted to FPGAs

NeurIPS 2023poster

High-level synthesis (HLS) aims to raise the abstraction layer in hardware design, enabling the design of domain-specific accelerators (DSAs) like field-programmable gate arrays (FPGAs) using C/C++ instead of hardware description languages (HDLs). Compiler directives in the form of pragmas play a cr…