1 citations · 3 across the 19 of their papers we have counts for
8 papers · 1 filter
Bern2Edge: A Neurosymbolic Compiler for Edge Deployment via Bernstein Polynomial Networks
Malak Gamal El-Din, Yifan Zhang, Yasser Shoukry +2
Deploying high-accuracy neural networks on resource-constrained edge devices remains challenging, as existing approaches treat training, compression, and hardware synthesis as sepa…
MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency
Saptarshi Mitra, Yifan Zhang, Rachid Karami +5
Mixture-of-Agents (MoA) systems improve reasoning accuracy by routing each query to multiple expert LLMs and aggregating their outputs. Efficiently executing this workload on limit…
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…
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…
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…
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…