180 citations · 349 across the 9 of their papers we have counts for
9 papers
HiHGNN: Accelerating HGNNs through Parallelism and Data Reusability Exploitation
Runzhen Xue, Dengke Han, Mingyu Yan +8
Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both st…
Accelerating Polynomial Multiplication for Homomorphic Encryption on GPUs
Kaustubh Shivdikar, Gilbert Jonatan, Evelio Mora +6
Homomorphic Encryption (HE) enables users to securely outsource both the storage and computation of sensitive data to untrusted servers. Not only does HE offer an attractive soluti…
Answer Fast: Accelerating BERT on the Tensor Streaming Processor
Ibrahim Ahmed, Sahil Parmar, Matthew Boyd +6
Transformers have become a predominant machine learning workload, they are not only the de-facto standard for natural language processing tasks, but they are also being deployed in…
ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse
Jongmin Kim, Gwangho Lee, Sangpyo Kim +4
Homomorphic Encryption (HE) is one of the most promising post-quantum cryptographic schemes that enable privacy-preserving computation on servers. However, noise accumulates as we…
BTS: An Accelerator for Bootstrappable Fully Homomorphic Encryption
Sangpyo Kim, Jongmin Kim, Michael Jaemin Kim +4
Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by providing computation on encrypted data (ciphertexts). HE is based on noisy encryption sch…
MGPU-TSM: A Multi-GPU System with Truly Shared Memory
Saiful A. Mojumder, Yifan Sun, Leila Delshadtehrani +6
The sizes of GPU applications are rapidly growing. They are exhausting the compute and memory resources of a single GPU, and are demanding the move to multiple GPUs. However, the p…