Publications (19)
Trojan Signatures in DNN Weights
Greg Fields, Mohammad Samragh, Mojan Javaheripi +2
Deep neural networks have been shown to be vulnerable to backdoor, or trojan, attacks where an adversary has embedded a trigger in the network at training time such that the model…
FastWave: Accelerating Autoregressive Convolutional Neural Networks on FPGA
Shehzeen Hussain, Mojan Javaheripi, Paarth Neekhara +2
Autoregressive convolutional neural networks (CNNs) have been widely exploited for sequence generation tasks such as audio synthesis, language modeling and neural machine translati…
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution
Hadi Pouransari, Mojan Javaheripi, Vinay Sharma +1
Knowledge distillation has been used to transfer knowledge learned by a sophisticated model (teacher) to a simpler model (student). This technique is widely used to compress model…
SWNet: Small-World Neural Networks and Rapid Convergence
Mojan Javaheripi, Bita Darvish Rouhani, Farinaz Koushanfar
Training large and highly accurate deep learning (DL) models is computationally costly. This cost is in great part due to the excessive number of trained parameters, which are well…
NetFlick: Adversarial Flickering Attacks on Deep Learning Based Video Compression
Jung-Woo Chang, Nojan Sheybani, Shehzeen Samarah Hussain +3
Video compression plays a significant role in IoT devices for the efficient transport of visual data while satisfying all underlying bandwidth constraints. Deep learning-based vide…
CodeX: Bit-Flexible Encoding for Streaming-based FPGA Acceleration of DNNs
Mohammad Samragh, Mojan Javaheripi, Farinaz Koushanfar
This paper proposes CodeX, an end-to-end framework that facilitates encoding, bitwidth customization, fine-tuning, and implementation of neural networks on FPGA platforms. CodeX in…