AERO: Softmax-Only LLMs for Efficient Private Inference Paper • 2410.13060 • Published Oct 16, 2024 • 4
AERO: Softmax-Only LLMs for Efficient Private Inference Paper • 2410.13060 • Published Oct 16, 2024 • 4 • 2
ReLU's Revival: On the Entropic Overload in Normalization-Free Large Language Models Paper • 2410.09637 • Published Oct 12, 2024 • 3 • 2
DeepReShape: Redesigning Neural Networks for Efficient Private Inference Paper • 2304.10593 • Published Apr 20, 2023
ReLU's Revival: On the Entropic Overload in Normalization-Free Large Language Models Paper • 2410.09637 • Published Oct 12, 2024 • 3
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning Paper • 2107.12342 • Published Jul 26, 2021
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale Paper • 2111.02583 • Published Nov 4, 2021
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance Paper • 2008.02565 • Published Aug 6, 2020
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs Paper • 2007.15248 • Published Jul 30, 2020
On the Demystification of Knowledge Distillation: A Residual Network Perspective Paper • 2006.16589 • Published Jun 30, 2020
DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator Paper • 2006.15103 • Published Jun 26, 2020
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks Paper • 2006.15102 • Published Jun 26, 2020
E2GC: Energy-efficient Group Convolution in Deep Neural Networks Paper • 2006.15100 • Published Jun 26, 2020