2 citations · 2 across the 5 of their papers we have counts for
6 papers
PerfDojo: Automated ML Library Generation for Heterogeneous Architectures
Andrei Ivanov, Siyuan Shen, Gioele Gottardo +5
The increasing complexity of machine learning models and the proliferation of diverse hardware architectures (CPUs, GPUs, accelerators) make achieving optimal performance a signifi…
VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores
Roberto L. Castro, Andrei Ivanov, Diego Andrade +3
The increasing success and scaling of Deep Learning models demands higher computational efficiency and power. Sparsification can lead to both smaller models as well as higher compu…
Cached Operator Reordering: A Unified View for Fast GNN Training
Julia Bazinska, Andrei Ivanov, Tal Ben-Nun +4
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However,…
TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization
Andrei Ivanov, Stefan Maria Ailuro
Polynomial regression is widely used and can help to express nonlinear patterns. However, considering very high polynomial orders may lead to overfitting and poor extrapolation abi…
STen: Productive and Efficient Sparsity in PyTorch
Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun +2
As deep learning models grow, sparsity is becoming an increasingly critical component of deep neural networks, enabling improved performance and reduced storage. However, existing…
A Data-Centric Optimization Framework for Machine Learning
Oliver Rausch, Tal Ben-Nun, Nikoli Dryden +3
Rapid progress in deep learning is leading to a diverse set of quickly changing models, with a dramatically growing demand for compute. However, as frameworks specialize performanc…