1 citations · 1 across the 8 of their papers we have counts for
8 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…
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…
From Loop Nests to Silicon: Mapping AI Workloads onto AMD NPUs with MLIR-AIR
Erwei Wang, Samuel Bayliss, Andra Bisca +19
General-purpose compilers abstract away parallelism, locality, and synchronization, limiting their effectiveness on modern spatial architectures. As modern computing architectures…
GAMA: High-Performance GEMM Acceleration on AMD Versal ML-Optimized AI Engines
Kaustubh Mhatre, Endri Taka, Aman Arora
General matrix-matrix multiplication (GEMM) is a fundamental operation in machine learning (ML) applications. We present the first comprehensive performance acceleration of GEMM wo…
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…
ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer Acceleration
Ning-Chi Huang, Chi-Chih Chang, Wei-Cheng Lin +3
sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing $N{…
Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs
Endri Taka, Dimitrios Gourounas, Andreas Gerstlauer +2
FPGAs are a promising platform for accelerating Deep Learning (DL) applications, due to their high performance, low power consumption, and reconfigurability. Recently, the leading…
MaxEVA: Maximizing the Efficiency of Matrix Multiplication on Versal AI Engine
Endri Taka, Aman Arora, Kai-Chiang Wu +1
The increasing computational and memory requirements of Deep Learning (DL) workloads has led to outstanding innovations in hardware architectures. An archetype of such architecture…