171 citations · 296 across the 16 of their papers we have counts for
4 papers · 1 filter
Architectural Implications of Embedding Dimension during GCN on CPU and GPU
Matthew Adiletta, David Brooks, Gu-Yeon Wei
Graph Neural Networks (GNNs) are a class of neural networks designed to extract information from the graphical structure of data. Graph Convolutional Networks (GCNs) are a widely u…
Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference
Woo-Seok Choi, Brandon Reagen, Gu-Yeon Wei +1
This paper proposes Impala, a new cryptographic protocol for private inference in the client-cloud setting. Impala builds upon recent solutions that combine the complementary stren…
OMU: A Probabilistic 3D Occupancy Mapping Accelerator for Real-time OctoMap at the Edge
Tianyu Jia, En-Yu Yang, Yu-Shun Hsiao +4
Autonomous machines (e.g., vehicles, mobile robots, drones) require sophisticated 3D mapping to perceive the dynamic environment. However, maintaining a real-time 3D map is expensi…
Trireme: Exploring Hierarchical Multi-Level Parallelism for Domain Specific Hardware Acceleration
Georgios Zacharopoulos, Adel Ejjeh, Ying Jing +9
The design of heterogeneous systems that include domain specific accelerators is a challenging and time-consuming process. While taking into account area constraints, designers mus…