148 citations · 511 across the 17 of their papers we have counts for
20 papers
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain +31
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a si…
DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling
Md Vasimuddin, Ramanarayan Mohanty, Sanchit Misra +1
Training Graph Neural Networks, on graphs containing billions of vertices and edges, at scale using minibatch sampling poses a key challenge: strong-scaling graphs and training exa…
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
Vasimuddin Md, Sanchit Misra, Guixiang Ma +6
Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. It…
AI Powered Compiler Techniques for DL Code Optimization
Sanket Tavarageri, Gagandeep Goyal, Sasikanth Avancha +2
Creating high performance implementations of deep learning primitives on CPUs is a challenging task. Multiple considerations including multi-level cache hierarchy, and wide SIMD un…
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads
Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha +16
During the past decade, novel Deep Learning (DL) algorithms, workloads and hardware have been developed to tackle a wide range of problems. Despite the advances in workload and har…
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks
Jacob R. Stevens, Dipankar Das, Sasikanth Avancha +2
Graph Neural Networks (GNNs) use a fully-connected layer to extract features from the nodes of a graph and aggregate these features using message passing between nodes, combining t…