3 citations · 6 across the 17 of their papers we have counts for
19 papers
Structure-augmented LLMs for High-Level Synthesis Pragma Optimization
Haocheng Xu, Ye Qiao, Phyo Pyae Moe Aung +7
Pragma insertion drives the quality of high-level synthesis (HLS) designs. Choosing the right directives demands expert knowledge and reasoning about loop nesting, data dependences…
EdgeDAE: Acceleration of Diffusion Action Experts for Real-Time Physical AI with Tiny VLAs on Edge FPGA-GPU Systems
Zhiheng Chen, Ye Qiao, Mohammad Abdullah Al Faruque +1
Physical AI models such as Vision-Language-Action (VLA) architectures enable generalist robotic policies through large-scale transformer backbones and diffusion-based action decode…
CurveFP: Co-Designing Numerical Representation and Product Arithmetic for Language Models
Ye Qiao
Low-precision formats usually optimize scalar fidelity while inheriting conventional product arithmetic. We introduce CurveFP, a block-scaled family that distributes magnitudes acr…
LowRank-SSM: Hardware-Software Co-Design for Rank-Reduced Mamba Acceleration on FPGA
Haocheng Xu, Bhardwaj Bhat, Yu-an Chou +6
State Space Models (SSMs) such as Mamba and Mamba-2 achieve linear-time autoregressive inference, making them attractive for latency-sensitive and resource-constrained deployment.…
A Better Start for Language Models: Domain-Conditional Position Offsets
Ye Qiao
Autoregressive language models are least accurate at the beginning of a sequence, where little context forces reliance on a generic pretraining prior. We show that this cold-start…
FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression
Ye Qiao, Yian Wang, Zhiheng Chen +2
Compressing large language models (LLMs) for deployment on commodity GPUs remains challenging: conventional scalar quantization is limited to fixed bit-widths (e.g., 8/4/3-bit), of…
PD-Swap: Prefill-Decode Logic Swapping for End-to-End LLM Inference on Edge FPGAs via Dynamic Partial Reconfiguration
Yifan Zhang, Zhiheng Chen, Ye Qiao +1
Aggressively quantized large language models (LLMs), such as BitNet-style 1.58-bit Transformers with ternary weights, make it feasible to deploy generative AI on low-power edge FPG…
TeLLMe v2: An Efficient End-to-End Ternary LLM Prefill and Decode Accelerator with Table-Lookup Matmul on Edge FPGAs
Ye Qiao, Zhiheng Chen, Yifan Zhang +2
With the emergence of wearable devices and other embedded systems, deploying large language models (LLMs) on edge platforms has become an urgent need. However, this is challenging…
Rethinking RoPE Scaling in Quantized LLM: Theory, Outlier, and Channel-Band Analysis with Weight Rescaling
Ye Qiao, Haocheng Xu, Xiaofan Zhang +1
Extending the context window support of large language models (LLMs) is crucial for tasks with long-distance dependencies. RoPE-based interpolation and extrapolation methods, such…
Q-ROAR: Outlier-Aware Rescaling for RoPE Position Interpolation in Quantized Long-Context LLMs
Ye Qiao, Sitao Huang
Extending LLM context windows is crucial for long range tasks. RoPE-based position interpolation (PI) methods like linear and frequency-aware scaling extend input lengths without r…
COBRA: Algorithm-Architecture Co-optimized Binary Transformer Accelerator for Edge Inference
Ye Qiao, Zhiheng Chen, Yian Wang +3
Transformer-based models have demonstrated superior performance in various fields, including natural language processing and computer vision. However, their enormous model size and…
TeLLMe: An Energy-Efficient Ternary LLM Accelerator for Prefilling and Decoding on Edge FPGAs
Ye Qiao, Zhiheng Chen, Yifan Zhang +2
Deploying large language models (LLMs) on edge platforms is challenged by their high computational and memory demands. Although recent low-bit quantization methods (e.g., BitNet, D…
Optimized Spatial Architecture Mapping Flow for Transformer Accelerators
Haocheng Xu, Faraz Tahmasebi, Ye Qiao +3
Recent innovations in Transformer-based large language models have significantly advanced the field of general-purpose neural language understanding and generation. With billions o…
MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs
Ye Qiao, Haocheng Xu, Yifan Zhang +1
Neural Architecture Search (NAS) has proven effective in discovering new Convolutional Neural Network (CNN) architectures, particularly for scenarios with well-defined accuracy opt…
RSEND: Retinex-based Squeeze and Excitation Network with Dark Region Detection for Efficient Low Light Image Enhancement
Jingcheng Li, Ye Qiao, Haocheng Xu +1
Images captured under low-light scenarios often suffer from low quality. Previous CNN-based deep learning methods often involve using Retinex theory. Nevertheless, most of them can…
TG-NAS: Generalizable Zero-Cost Proxies with Operator Description Embedding and Graph Learning for Efficient Neural Architecture Search
Ye Qiao, Jingcheng Li, Haocheng Xu +1
Neural Architecture Search (NAS) is a powerful technique for discovering high-performing CNN architectures, but most existing methods rely on costly training or extensive sampling.…
MicroNAS: Zero-Shot Neural Architecture Search for MCUs
Ye Qiao, Haocheng Xu, Yifan Zhang +1
Neural Architecture Search (NAS) effectively discovers new Convolutional Neural Network (CNN) architectures, particularly for accuracy optimization. However, prior approaches often…
Support for Stock Trend Prediction Using Transformers and Sentiment Analysis
Harsimrat Kaeley, Ye Qiao, Nader Bagherzadeh
Stock trend analysis has been an influential time-series prediction topic due to its lucrative and inherently chaotic nature. Many models looking to accurately predict the trend of…
A Two-Stage Efficient 3-D CNN Framework for EEG Based Emotion Recognition
Ye Qiao, Mohammed Alnemari, Nader Bagherzadeh
This paper proposes a novel two-stage framework for emotion recognition using EEG data that outperforms state-of-the-art models while keeping the model size small and computational…