7 papers
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…
QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models
Tien-Yu Chi, Hung-Yueh Chiang, Diana Marculescu +1
State space models (SSMs) reduce the quadratic complexity of transformers by leveraging linear recurrence. Recently, VMamba has emerged as a strong SSM-based vision backbone, yet r…
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…
Systolic Sparse Tensor Slices: FPGA Building Blocks for Sparse and Dense AI Acceleration
Endri Taka, Ning-Chi Huang, Chi-Chih Chang +3
FPGA architectures have recently been enhanced to meet the substantial computational demands of modern deep neural networks (DNNs). To this end, both FPGA vendors and academic rese…