Search Results for author:
Found 7 papers, 2 papers with code
High-Dimensional Yield Estimation using Shrinkage Deep Features and Maximization of Integral Entropy Reduction
Despite the fast advances in high-sigma yield analysis with the help of machine learning techniques in the past decade, one of the main challenges, the curse of dimensionality, which is inevitable when dealing with modern large-scale circuits, remains unsolved.
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective
The same data are propagated through the graph structure to perform the same neural operation multiple times in GNNs, leading to redundant computation which accounts for 92. 4% of total operators.
CogDL: A Toolkit for Deep Learning on Graphs
Deep learning on graphs has attracted tremendous attention from the graph learning community in recent years.
Explore the Potential of CNN Low Bit Training
Through this dynamic precision framework, we can reduce the bit-width of convolution, which is the most computational cost, while keeping the training process close to the full precision floating-point training.
GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks
GE-SpMM performs SpMM-like operation on sparse matrices represented in the most common Compressed Sparse Row (CSR) format, so it can be embedded in GNN frameworks with no preprocessing overheads and support general GNN algorithms.
Distributed, Parallel, and Cluster Computing
Exploring the Potential of Low-bit Training of Convolutional Neural Networks
For training a variety of models on CIFAR-10, using 1-bit mantissa and 2-bit exponent is adequate to keep the accuracy loss within $1\%$.
Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud
Currently, the majority of FPGA-based DNN accelerators in the cloud run in a time-division multiplexing way for multiple users sharing a single FPGA, and require re-compilation with $\sim$100 s overhead.
