1 citations · 1 across the 3 of their papers we have counts for
3 papers
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…
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…