63 citations · 255 across the 20 of their papers we have counts for
20 papers
Model Extraction Attacks on Split Federated Learning
Jingtao Li, Adnan Siraj Rakin, Xing Chen +4
Federated Learning (FL) is a popular collaborative learning scheme involving multiple clients and a server. FL focuses on protecting clients' data but turns out to be highly vulner…
Accelerating Graph Analytics on a Reconfigurable Architecture with a Data-Indirect Prefetcher
Yichen Yang, Jingtao Li, Nishil Talati +5
The irregular nature of memory accesses of graph workloads makes their performance poor on modern computing platforms. On manycore reconfigurable architectures (MRAs), in particula…
Profile-Guided Parallel Task Extraction and Execution for Domain Specific Heterogeneous SoC
Liangliang Chang, Joshua Mack, Benjamin Willis +4
In this study, we introduce a methodology for automatically transforming user applications in the radar and communication domain written in C/C++ based on dynamic profiling to a pa…
Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures
Shunyao Wu, Chaitali Chakrabarti, Ahmed Alkhateeb
Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. To address this chall…
An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data
Jingtao Li, Jian Zhou, Yan Xiong +2
Sampling is an essential part of raw point cloud data processing such as in the popular PointNet++ scheme. Farthest Point Sampling (FPS), which iteratively samples the farthest poi…
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
Jingtao Li, Adnan Siraj Rakin, Xing Chen +3
This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activat…
LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems
Shunyao Wu, Chaitali Chakrabarti, Ahmed Alkhateeb
Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. This paper proposes t…
Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration
Shunyao Wu, Muhammad Alrabeiah, Chaitali Chakrabarti +1
Overcoming the link blockage challenges is essential for enhancing the reliability and latency of millimeter wave (mmWave) and sub-terahertz (sub-THz) communication networks. Previ…
SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks
Gokul Krishnan, Sumit K. Mandal, Manvitha Pannala +4
In-memory computing (IMC) on a monolithic chip for deep learning faces dramatic challenges on area, yield, and on-chip interconnection cost due to the ever-increasing model sizes.…
Versa: A Dataflow-Centric Multiprocessor with 36 Systolic ARM Cortex-M4F Cores and a Reconfigurable Crossbar-Memory Hierarchy in 28nm
Sung Kim, Morteza Fayazi, Alhad Daftardar +10
We present Versa, an energy-efficient processor with 36 systolic ARM Cortex-M4F cores and a runtime-reconfigurable memory hierarchy. Versa exploits algorithm-specific characteristi…
NeurObfuscator: A Full-stack Obfuscation Tool to Mitigate Neural Architecture Stealing
Jingtao Li, Zhezhi He, Adnan Siraj Rakin +2
Neural network stealing attacks have posed grave threats to neural network model deployment. Such attacks can be launched by extracting neural architecture information, such as lay…
Communication and Computation Reduction for Split Learning using Asynchronous Training
Xing Chen, Jingtao Li, Chaitali Chakrabarti
Split learning is a promising privacy-preserving distributed learning scheme that has low computation requirement at the edge device but has the disadvantage of high communication…
Impact of On-Chip Interconnect on In-Memory Acceleration of Deep Neural Networks
Gokul Krishnan, Sumit K. Mandal, Chaitali Chakrabarti +3
With the widespread use of Deep Neural Networks (DNNs), machine learning algorithms have evolved in two diverse directions -- one with ever-increasing connection density for better…
RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy
Adnan Siraj Rakin, Li Yang, Jingtao Li +5
Recently developed adversarial weight attack, a.k.a. bit-flip attack (BFA), has shown enormous success in compromising Deep Neural Network (DNN) performance with an extremely small…
Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements
Shunyao Wu, Muhammad Alrabeiah, Andrew Hredzak +2
Millimeter wave (mmWave) communication is a key component of 5G and beyond. Harvesting the gains of the large bandwidth and low latency at mmWave systems, however, is challenged by…
RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery
Jingtao Li, Adnan Siraj Rakin, Zhezhi He +2
Adversarial attacks on Neural Network weights, such as the progressive bit-flip attack (PBFA), can cause a catastrophic degradation in accuracy by flipping a very small number of b…
T-BFA: Targeted Bit-Flip Adversarial Weight Attack
Adnan Siraj Rakin, Zhezhi He, Jingtao Li +3
Traditional Deep Neural Network (DNN) security is mostly related to the well-known adversarial input example attack. Recently, another dimension of adversarial attack, namely, atta…
Automated Parallel Kernel Extraction from Dynamic Application Traces
Richard Uhrie, Chaitali Chakrabarti, John Brunhaver
Modern program runtime is dominated by segments of repeating code called kernels. Kernels are accelerated by increasing memory locality, increasing data-parallelism, and exploiting…
Minimizing Area and Energy of Deep Learning Hardware Design Using Collective Low Precision and Structured Compression
Shihui Yin, Gaurav Srivastava, Shreyas K. Venkataramanaiah +3
Deep learning algorithms have shown tremendous success in many recognition tasks; however, these algorithms typically include a deep neural network (DNN) structure and a large numb…
Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations
Shihui Yin, Shreyas K. Venkataramanaiah, Gregory K. Chen +4
We present a new back propagation based training algorithm for discrete-time spiking neural networks (SNN). Inspired by recent deep learning algorithms on binarized neural networks…