11 papers
DARE: Diffusion Language Model Activation Reuse for Efficient Inference
Natalia Frumkin, Bokun Wang, Hung-Yueh Chiang +3
Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to auto-regressive (AR) models, offering greater expressive capacity and potential for parallel gene…
UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs
Hung-Yueh Chiang, Chi-Chih Chang, Yu-Chen Lu +4
Deploying large language models (LLMs) on mobile platforms faces significant challenges due to the limited memory and shared computational resources of the device. Resource availab…
Striking the Balance: GEMM Performance Optimization Across Generations of Ryzen AI NPUs
Endri Taka, Andre Roesti, Joseph Melber +3
The high computational and memory demands of modern deep learning (DL) workloads have led to the development of specialized hardware devices from cloud to edge, such as AMD's Ryzen…
ELANA: A Simple Energy and Latency Analyzer for LLMs
Hung-Yueh Chiang, Bokun Wang, Diana Marculescu
The latency and power consumption of large language models (LLMs) are major constraints when serving them across a wide spectrum of hardware platforms, from mobile edge devices to…
Can Asymmetric Tile Buffering Be Beneficial?
Chengyue Wang, Wesley Pang, Xinrui Wu +9
General matrix multiplication (GEMM) is the computational backbone of modern AI workloads, and its efficiency is critically dependent on effective tiling strategies. Conventional a…
Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models
Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin +3
State Space Models (SSMs) are emerging as a compelling alternative to Transformers because of their consistent memory usage and high performance. Despite this, scaling up SSMs on c…