32 citations · 39 across the 9 of their papers we have counts for
9 papers
Scaling Muon for Diffusion Transformers
Chenghao Li, Xiao Han, Xinxin Huang +22
The matrix-aware optimizer Muon improves large model training by balancing updates across singular directions, yet its scaling behavior and end-to-end efficiency on large Diffusion…
LoyalDiffusion: A Diffusion Model Guarding Against Data Replication
Chenghao Li, Yuke Zhang, Dake Chen +2
Diffusion models have demonstrated significant potential in image generation. However, their ability to replicate training data presents a privacy risk, particularly when the train…
Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement
Chenghao Li, Dake Chen, Yuke Zhang +1
While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns. Although recent…
Island-based Random Dynamic Voltage Scaling vs ML-Enhanced Power Side-Channel Attacks
Dake Chen, Christine Goins, Maxwell Waugaman +2
In this paper, we describe and analyze an island-based random dynamic voltage scaling (iRDVS) approach to thwart power side-channel attacks. We first analyze the impact of the numb…
Unraveling Latch Locking Using Machine Learning, Boolean Analysis, and ILP
Dake Chen, Xuan Zhou, Yinghua Hu +5
Logic locking has become a promising approach to provide hardware security in the face of a possibly insecure fabrication supply chain. While many techniques have focused on lockin…
Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference
Souvik Kundu, Yuke Zhang, Dake Chen +1
Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference. In this paper, we present a model optimiza…
C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference
Yuke Zhang, Dake Chen, Souvik Kundu +3
Recently, private inference (PI) has addressed the rising concern over data and model privacy in machine learning inference as a service. However, existing PI frameworks suffer fro…
Fun-SAT: Functional Corruptibility-Guided SAT-Based Attack on Sequential Logic Encryption
Yinghua Hu, Yuke Zhang, Kaixin Yang +3
The SAT attack has shown to be efficient against most combinational logic encryption methods. It can be extended to attack sequential logic encryption techniques by leveraging circ…
GF-Flush: A GF(2) Algebraic Attack on Secure Scan Chains
Dake Chen, Chunxiao Lin, Peter A. Beerel
Scan chains provide increased controllability and observability for testing digital circuits. The increased testability, however, can also be a source of information leakage for se…