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Endri-Andreas Taka - PHD Student - LinkedIn Graduate Researcher at Energy-Aware Computing Group (EnyAC) led by Diana Marculescu • Developed a framework to maximize the efficiency of Matrix Multiplication-based Deep Learning
Endri-Andreas Taka - Google Scholar Univeristy of Texas at Austin Verified email at utexas edu HW ML Co-design FPGAs Energy-aware Computing Low Power Design Articles 1–11
Diana Marculescu - Students Tanvir Mahmud Endri Taka M S Students Undergraduate Students Grace Kim Richard Yuan Recent Collaborators Kai-Chiang (Alex) Wu (Professor, National Yang Ming Chiao Tung University) Ahmet Fatih Inci (Machine Learning Engineer, Apple) Ting-Wu (Rudy) Chin (Quantitative Researcher, Citadel Securities) Ruizhou Ding (Quantitative Researcher, Citadel
Endri Taka (0009-0000-5136-7580) - ORCID ORCID record for Endri Taka ORCID provides an identifier for individuals to use with their name as they engage in research, scholarship, and innovation activities
Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs A {endri taka, dimitrisgrn, gerstl, dianam}@utexas edu, aman kbm@asu edu Abstract—FPGAs are a promising platform for accelerating Deep Learning (DL) applications, due to their high performance, low power consumption, and reconfigurability Recently, the leading FPGA vendors have enhanced their architectu
Systolic Sparse Tensor Slices: FPGA Building Blocks for Sparse and . . . To address this limitation, we propose incorporating structured sparsity support into FPGA architectures We architect 2D systolic in-fabric blocks, named systolic sparse tensor (SST) slices, that support multiple degrees of sparsity to eficiently accelerate a wide variety of DNNs
Endri-Andreas Taka - - Research Endri-Andreas Taka Contact Member See Phone Number Call: +1 512-471-1166 View Member Profile Austin, Texas View On Larger Map