35 citations · 130 across the 50 of their papers we have counts for
50 papers
HE^2: A Communication-Light Heterogeneous Architecture for Efficient Fully Homomorphic Encryption
Shangyi Shi, Husheng Han, Zhaoxuan Kan +8
CKKS, an emerging fully homomorphic encryption (FHE) scheme, has been promising in privacy-preserving applications by enabling SIMD fixed-point computations on ciphertexts. Despite…
Discrete Diffusion for Complex and Congested Multi-Agent Path Finding with Sparse Social Attention
Yuanzhe Wang, Tian Zhi, Zihang Wei +8
Multi-Agent Path Finding (MAPF) is a coordination problem that requires computing globally consistent, collision-free trajectories from individual start positions to assigned goal…
LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction
Enshuai Zhou, Yifan Hao, Chao Wang +7
Long-context inference in Large Language Models (LLMs) is bottlenecked by the linear growth of Key-Value (KV) cache memory. Existing KV cache compression paradigms are fundamentall…
QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization
Changxin Ke, Rui Zhang, Jiaming Guo +10
Large Language Models (LLMs) achieve strong program repair performance but often suffer from over-editing, where excessive modifications overwrite correct code and hinder bug local…
QiMeng-ChipV-RTL: Exploiting Information Locality for IP-level Verilog Generation
Hanqi Lyu, Di Huang, Yaoyu Zhu +10
The generation of Register-Transfer Level (RTL) code is a crucial yet labor-intensive step in digital hardware design, traditionally requiring engineers to manually translate compl…
Efficient Diffusion Planning with Temporal Diffusion
Jiaming Guo, Rui Zhang, Zerun Li +7
Diffusion planning is a promising method for learning high-performance policies from offline data. To avoid the impact of discrepancies between planning and reality on performance,…
QiMeng-CRUX: Narrowing the Gap Between Natural Language and Verilog via Core Refined Understanding eXpression for Circuit Design
Lei Huang, Rui Zhang, Jiaming Guo +9
Large language models (LLMs) have shown promising capabilities in hardware description language (HDL) generation. However, existing approaches often rely on free-form natural langu…
QiMeng-SALV: Signal-Aware Learning for Verilog Code Generation
Yang Zhang, Rui Zhang, Jiaming Guo +10
The remarkable progress of Large Language Models (LLMs) presents promising opportunities for Verilog code generation which is significantly important for automated circuit design.…
Code Driven Planning with Domain-Adaptive Critic
Zikang Tian, Shaohui Peng, Du Huang +11
Large Language Models (LLMs) have been widely adopted as task planners for AI agents in sequential decision-making problems, leveraging their extensive world knowledge. However, th…
Hardwired-Neurons Language Processing Units as General-Purpose Cognitive Substrates
Yang Liu, Yi Chen, Yongwei Zhao +24
The rapid advancement of Large Language Models (LLMs) has established language as a core general-purpose cognitive substrate, driving the demand for specialized Language Processing…
StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs through Knowledge-Reasoning Fusion
Yutong Wu, Di Huang, Ruosi Wan +8
Autoformalization aims to translate natural-language mathematical statements into a formal language. While LLMs have accelerated progress in this area, existing methods still suffe…
RealBench: Benchmarking Verilog Generation Models with Real-World IP Designs
Pengwei Jin, Di Huang, Chongxiao Li +10
The automatic generation of Verilog code using Large Language Models (LLMs) has garnered significant interest in hardware design automation. However, existing benchmarks for evalua…
QiMeng-CPU-v2: Automated Superscalar Processor Design by Learning Data Dependencies
Shuyao Cheng, Rui Zhang, Wenkai He +10
Automated processor design, which can significantly reduce human efforts and accelerate design cycles, has received considerable attention. While recent advancements have automatic…
QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation
Yaoyu Zhu, Di Huang, Hanqi Lyu +16
Large language models (LLMs) trained via reinforcement learning with verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit, automatable verification, such as…
FlexMem: High-Parallel Near-Memory Architecture for Flexible Dataflow in Fully Homomorphic Encryption
Shangyi Shi, Husheng Han, Jianan Mu +7
Fully Homomorphic Encryption (FHE) imposes substantial memory bandwidth demands, presenting significant challenges for efficient hardware acceleration. Near-memory Processing (NMP)…
AGON: Automated Design Framework for Customizing Processors from ISA Documents
Chongxiao Li, Di Huang, Pengwei Jin +12
Customized processors are attractive solutions for vast domain-specific applications due to their high energy efficiency. However, designing a processor in traditional flows is tim…
InputSnatch: Stealing Input in LLM Services via Timing Side-Channel Attacks
Xinyao Zheng, Husheng Han, Shangyi Shi +4
Large language models (LLMs) possess extensive knowledge and question-answering capabilities, having been widely deployed in privacy-sensitive domains like finance and medical cons…
Cambricon-LLM: A Chiplet-Based Hybrid Architecture for On-Device Inference of 70B LLM
Zhongkai Yu, Shengwen Liang, Tianyun Ma +12
Deploying advanced large language models on edge devices, such as smartphones and robotics, is a growing trend that enhances user data privacy and network connectivity resilience w…
TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computing
Husheng Han, Xinyao Zheng, Yuanbo Wen +11
Heterogeneous collaborative computing with NPU and CPU has received widespread attention due to its substantial performance benefits. To ensure data confidentiality and integrity d…
CodeV: Empowering LLMs with HDL Generation through Multi-Level Summarization
Yang Zhao, Di Huang, Chongxiao Li +14
The design flow of processors, particularly in hardware description languages (HDL) like Verilog and Chisel, is complex and costly. While recent advances in large language models (…
InverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-Instruct
Yutong Wu, Di Huang, Wenxuan Shi +13
Recent advancements in open-source code large language models (LLMs) have been driven by fine-tuning on the data generated from powerful closed-source LLMs, which are expensive to…
Prompt-based Visual Alignment for Zero-shot Policy Transfer
Haihan Gao, Rui Zhang, Qi Yi +13
Overfitting in RL has become one of the main obstacles to applications in reinforcement learning(RL). Existing methods do not provide explicit semantic constrain for the feature ex…
Luban: Building Open-Ended Creative Agents via Autonomous Embodied Verification
Yuxuan Guo, Shaohui Peng, Jiaming Guo +15
Building open agents has always been the ultimate goal in AI research, and creative agents are the more enticing. Existing LLM agents excel at long-horizon tasks with well-defined…
Assessing and Understanding Creativity in Large Language Models
Yunpu Zhao, Rui Zhang, Wenyi Li +10
In the field of natural language processing, the rapid development of large language model (LLM) has attracted more and more attention. LLMs have shown a high level of creativity i…