5 citations · 10 across the 8 of their papers we have counts for
8 papers
Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training
Zihao Deng, Benjamin Ghaemmaghami, Ashish Kumar Singh +4
Modern DNN-based recommendation systems rely on training-derived embeddings of sparse features. Input sparsity makes obtaining high-quality embeddings for rarely-occurring categori…
Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic Programming
Zihao Deng, Sayeh Sharify, Xin Wang +1
Quantization is a widely used technique to compress neural networks. Assigning uniform bit-widths across all layers can result in significant accuracy degradation at low precision…
A Provably Secure Strong PUF based on LWE: Construction and Implementation
Xiaodan Xi, Ge Li, Ye Wang +2
We construct a strong PUF with provable security against ML attacks on both classical and quantum computers. The security is guaranteed by the cryptographic hardness of learning de…
Variability-Aware Training and Self-Tuning of Highly Quantized DNNs for Analog PIM
Zihao Deng, Michael Orshansky
DNNs deployed on analog processing in memory (PIM) architectures are subject to fabrication-time variability. We developed a new joint variability- and quantization-aware DNN train…
Power-Based Attacks on Spatial DNN Accelerators
Ge Li, Mohit Tiwari, Michael Orshansky
With proliferation of DNN-based applications, the confidentiality of DNN model is an important commercial goal. Spatial accelerators, that parallelize matrix/vector operations, are…
Training with Multi-Layer Embeddings for Model Reduction
Benjamin Ghaemmaghami, Zihao Deng, Benjamin Cho +4
Modern recommendation systems rely on real-valued embeddings of categorical features. Increasing the dimension of embedding vectors improves model accuracy but comes at a high cost…