13 citations · 51 across the 19 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…
SpeedLimit: Neural Architecture Search for Quantized Transformer Models
Yuji Chai, Luke Bailey, Yunho Jin +5
While research in the field of transformer models has primarily focused on enhancing performance metrics such as accuracy and perplexity, practical applications in industry often n…
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…
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference
Maximilian Lam, Michael Mitzenmacher, Vijay Janapa Reddi +2
Multiparty computation approaches to secure neural network inference commonly rely on garbled circuits for securely executing nonlinear activation functions. However, garbled circu…