45 citations · 50 across the 8 of their papers we have counts for
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cs.AR2023
Enabling Accelerators for Graph Computing
Kaustubh Shivdikar
The advent of Graph Neural Networks (GNNs) has revolutionized the field of machine learning, offering a novel paradigm for learning on graph-structured data. Unlike traditional neu…
cs.LG2023★ 3 cited
MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training
Hongwu Peng, Xi Xie, Kaustubh Shivdikar +6
In the acceleration of deep neural network training, the GPU has become the mainstream platform. GPUs face substantial challenges on GNNs, such as workload imbalance and memory acc…
cs.CR2023★ 45 cited
GME: GPU-based Microarchitectural Extensions to Accelerate Homomorphic Encryption
Kaustubh Shivdikar, Yuhui Bao, Rashmi Agrawal +9
Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without decrypting it. FHE has garnered significant attention over the past decade as it supports secure…