17 citations · 37 across the 4 of their papers we have counts for
6 papers
CLEANN: Accelerated Trojan Shield for Embedded Neural Networks
Mojan Javaheripi, Mohammad Samragh, Gregory Fields +2
We propose CLEANN, the first end-to-end framework that enables online mitigation of Trojans for embedded Deep Neural Network (DNN) applications. A Trojan attack works by injecting…
GeneCAI: Genetic Evolution for Acquiring Compact AI
Mojan Javaheripi, Mohammad Samragh, Tara Javidi +1
In the contemporary big data realm, Deep Neural Networks (DNNs) are evolving towards more complex architectures to achieve higher inference accuracy. Model compression techniques c…
ASCAI: Adaptive Sampling for acquiring Compact AI
Mojan Javaheripi, Mohammad Samragh, Tara Javidi +1
This paper introduces ASCAI, a novel adaptive sampling methodology that can learn how to effectively compress Deep Neural Networks (DNNs) for accelerated inference on resource-cons…
XONN: XNOR-based Oblivious Deep Neural Network Inference
M. Sadegh Riazi, Mohammad Samragh, Hao Chen +3
Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learni…
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…
RAPIDNN: In-Memory Deep Neural Network Acceleration Framework
Mohsen Imani, Mohammad Samragh, Yeseong Kim +3
Deep neural networks (DNN) have demonstrated effectiveness for various applications such as image processing, video segmentation, and speech recognition. Running state-of-the-art D…