6 papers
Proteus: Preserving Model Confidentiality during Graph Optimizations
Yubo Gao, Maryam Haghifam, Christina Giannoula +3
Deep learning (DL) models have revolutionized numerous domains, yet optimizing them for computational efficiency remains a challenging endeavor. Development of new DL models typica…
ACS: Concurrent Kernel Execution on Irregular, Input-Dependent Computational Graphs
Sankeerth Durvasula, Adrian Zhao, Raymond Kiguru +3
GPUs are widely used to accelerate many important classes of workloads today. However, we observe that several important emerging classes of workloads, including simulation engines…
ENVIDR: Implicit Differentiable Renderer with Neural Environment Lighting
Ruofan Liang, Huiting Chen, Chunlin Li +3
Recent advances in neural rendering have shown great potential for reconstructing scenes from multiview images. However, accurately representing objects with glossy surfaces remain…
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception
Sankeerth Durvasula, Yushi Guan, Nandita Vijaykumar
Event cameras capture visual information with a high temporal resolution and a wide dynamic range. This enables capturing visual information at fine time granularities (e.g., micro…
Architectural Support for Efficient Data Movement in Disaggregated Systems
Christina Giannoula, Kailong Huang, Jonathan Tang +4
Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a serve…
DaeMon: Architectural Support for Efficient Data Movement in Disaggregated Systems
Christina Giannoula, Kailong Huang, Jonathan Tang +4
Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a serve…