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Found 6 papers, 1 papers with code
Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators
The algorithms and accelerator cost models are connected via a novel mapping abstraction that captures the map space of spatial accelerators which can be systematically pruned based on constraints from the hardware, workload, and mapper.
Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication
There is a growing interest in custom spatial accelerators for machine learning applications.
Concentric Spherical GNN for 3D Representation Learning
Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry.
Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks
We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost.
How Robust Are Graph Neural Networks to Structural Noise?
Graph neural networks (GNNs) are an emerging model for learning graph embeddings and making predictions on graph structured data.
An Algebraic Sparsified Nested Dissection Algorithm Using Low-Rank Approximations
We evaluate the algorithm on some large problems show it exhibits near-linear scaling.
Numerical Analysis
