4 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…