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20202026
most citedArrayFlex: A Systolic Array Architecture with Configurable Transparent Pipelining

19 citations · 73 across the 28 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.AR2024

GCN-ABFT: Low-Cost Online Error Checking for Graph Convolutional Networks

Christodoulos Peltekis, Giorgos Dimitrakopoulos

Graph convolutional networks (GCNs) are popular for building machine-learning application for graph-structured data. This widespread adoption led to the development of specialized…

cs.AR2024

Online Alignment and Addition in Multi-Term Floating-Point Adders

Kosmas Alexandridis, Giorgos Dimitrakopoulos

Multi-term floating-point addition appears in vector dot-product computations, matrix multiplications, and other forms of floating-point data aggregation. A critical step in multi-…

cs.AR2024

Floating-Point Multiply-Add with Approximate Normalization for Low-Cost Matrix Engines

Kosmas Alexandridis, Christodoulos Peltekis, Dionysios Filippas +1

The widespread adoption of machine learning algorithms necessitates hardware acceleration to ensure efficient performance. This acceleration relies on custom matrix engines that op…

cs.AR2024

Error Checking for Sparse Systolic Tensor Arrays

Christodoulos Peltekis, Dionysios Filippas, Giorgos Dimitrakopoulos

Structured sparsity is an efficient way to prune the complexity of modern Machine Learning (ML) applications and to simplify the handling of sparse data in hardware. In such cases,…

cs.AR2024

Reusing Softmax Hardware Unit for GELU Computation in Transformers

Christodoulos Peltekis, Kosmas Alexandridis, Giorgos Dimitrakopoulos

Transformers have improved drastically the performance of natural language processing (NLP) and computer vision applications. The computation of transformers involves matrix multip…

cs.AR2024★ 4 cited

DeMM: A Decoupled Matrix Multiplication Engine Supporting Relaxed Structured Sparsity

Christodoulos Peltekis, Vasileios Titopoulos, Chrysostomos Nicopoulos +1

Deep Learning (DL) has achieved unprecedented success in various application domains. Meanwhile, model pruning has emerged as a viable solution to reduce the footprint of DL models…