1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…