1 citations · 3 across the 19 of their papers we have counts for
6 papers · 2 filters
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…
A Sparsity-Aware Autonomous Path Planning Accelerator with HW/SW Co-Design and Multi-Level Dataflow Optimization
Yifan Zhang, Xiaoyu Niu, Hongzheng Tian +4
Path planning is critical for autonomous driving, generating smooth, collision-free, feasible paths based on perception and localization inputs. However, its computationally intens…
Characterizing State Space Model and Hybrid Language Model Performance with Long Context
Saptarshi Mitra, Rachid Karami, Haocheng Xu +2
Emerging applications such as AR are driving demands for machine intelligence capable of processing continuous and/or long-context inputs on local devices. However, currently domin…
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…