297 citations · 308 across the 8 of their papers we have counts for
8 papers
MF-QAT: Multi-Format Quantization-Aware Training for Elastic Inference
Zifei Xu, Sayeh Sharify, Hesham Mostafa
Quantization-aware training (QAT) is typically performed for a single target numeric format, while practical deployments often need to choose numerical precision at inference time…
Distributed Training of Large Graph Neural Networks with Variable Communication Rates
Juan Cervino, Md Asadullah Turja, Hesham Mostafa +2
Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to the large memory and computing requirements. Distributed GNN training, where the graph is pa…
Fully-inductive Node Classification on Arbitrary Graphs
Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin +3
One fundamental challenge in graph machine learning is generalizing to new graphs. Many existing methods following the inductive setup can generalize to test graphs with new struct…
PARSAC: Fast, Human-quality Floorplanning for Modern SoCs with Complex Design Constraints
Hesham Mostafa, Uday Mallappa, Mikhail Galkin +2
The floorplanning of Systems-on-a-Chip (SoCs) and of chip sub-systems is a crucial step in the physical design flow as it determines the optimal shapes and locations of the blocks…
FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCs
Uday Mallappa, Hesham Mostafa, Mikhail Galkin +2
Floorplanning for systems-on-a-chip (SoCs) and its sub-systems is a crucial and non-trivial step of the physical design flow. It represents a difficult combinatorial optimization p…
FastSample: Accelerating Distributed Graph Neural Network Training for Billion-Scale Graphs
Hesham Mostafa, Adam Grabowski, Md Asadullah Turja +3
Training Graph Neural Networks(GNNs) on a large monolithic graph presents unique challenges as the graph cannot fit within a single machine and it cannot be decomposed into smaller…
Towards Foundation Models for Knowledge Graph Reasoning
Mikhail Galkin, Xinyu Yuan, Hesham Mostafa +2
Foundation models in language and vision have the ability to run inference on any textual and visual inputs thanks to the transferable representations such as a vocabulary of token…
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)
Jacques Kaiser, Hesham Mostafa, Emre Neftci
A growing body of work underlines striking similarities between biological neural networks and recurrent, binary neural networks. A relatively smaller body of work, however, discus…