19 citations · 43 across the 12 of their papers we have counts for
12 papers
Periodic Online Testing for Sparse Systolic Tensor Arrays
Christodoulos Peltekis, Chrysostomos Nicopoulos, Giorgos Dimitrakopoulos
Modern Machine Learning (ML) applications often benefit from structured sparsity, a technique that efficiently reduces model complexity and simplifies handling of sparse data in ha…
Optimizing Structured-Sparse Matrix Multiplication in RISC-V Vector Processors
Vasileios Titopoulos, Kosmas Alexandridis, Christodoulos Peltekis +2
Structured sparsity has been proposed as an efficient way to prune the complexity of Machine Learning (ML) applications and to simplify the handling of sparse data in hardware. Acc…
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…
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…
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,…
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…
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…
IndexMAC: A Custom RISC-V Vector Instruction to Accelerate Structured-Sparse Matrix Multiplications
V. Titopoulos, K. Alexandridis, C. Peltekis +2
Structured sparsity has been proposed as an efficient way to prune the complexity of modern Machine Learning (ML) applications and to simplify the handling of sparse data in hardwa…
The Case for Asymmetric Systolic Array Floorplanning
C. Peltekis, D. Filippas, G. Dimitrakopoulos +1
The widespread proliferation of deep learning applications has triggered the need to accelerate them directly in hardware. General Matrix Multiplication (GEMM) kernels are elementa…
Low-Power Data Streaming in Systolic Arrays with Bus-Invert Coding and Zero-Value Clock Gating
C. Peltekis, D. Filippas, G. Dimitrakopoulos +1
Systolic Array (SA) architectures are well suited for accelerating matrix multiplications through the use of a pipelined array of Processing Elements (PEs) communicating with local…
Reduced-Precision Floating-Point Arithmetic in Systolic Arrays with Skewed Pipelines
D. Filippas, C. Peltekis, G. Dimitrakopoulos +1
The acceleration of deep-learning kernels in hardware relies on matrix multiplications that are executed efficiently on Systolic Arrays (SA). To effectively trade off deep-learning…
ArrayFlex: A Systolic Array Architecture with Configurable Transparent Pipelining
C. Peltekis, D. Filippas, G. Dimitrakopoulos +2
Convolutional Neural Networks (CNNs) are the state-of-the-art solution for many deep learning applications. For maximum scalability, their computation should combine high performan…