11 citations · 41 across the 18 of their papers we have counts for
19 papers
Emulated Integrity Replica: Enabling Self-Healing on FPGA SoCs via Hierarchical Twins
Arsalan Ali Malik, Ali Suvizi, Guru Venkataramani +1
Convolutional neural networks (CNNs) are increasingly being deployed on system-on-chip (SoC) platforms, where hardware-accelerated inference enables low-latency edge computing. Ach…
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems
Kailash Gogineni, Sai Santosh Dayapule, Juan Gómez-Luna +6
Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward signals from experience datasets. However, RL training often faces memory limitations, lead…
Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
Hanhan Zhou, Tian Lan, Guru Venkataramani +1
Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due…
AccMER: Accelerating Multi-Agent Experience Replay with Cache Locality-aware Prioritization
Kailash Gogineni, Yongsheng Mei, Peng Wei +2
Multi-Agent Experience Replay (MER) is a key component of off-policy reinforcement learning~(RL) algorithms. By remembering and reusing experiences from the past, experience replay…
Towards Efficient Multi-Agent Learning Systems
Kailash Gogineni, Peng Wei, Tian Lan +1
Multi-Agent Reinforcement Learning (MARL) is an increasingly important research field that can model and control multiple large-scale autonomous systems. Despite its achievements,…
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization
Yongsheng Mei, Hanhan Zhou, Tian Lan +2
Experience replay is crucial for off-policy reinforcement learning (RL) methods. By remembering and reusing the experiences from past different policies, experience replay signific…
Scalability Bottlenecks in Multi-Agent Reinforcement Learning Systems
Kailash Gogineni, Peng Wei, Tian Lan +1
Multi-Agent Reinforcement Learning (MARL) is a promising area of research that can model and control multiple, autonomous decision-making agents. During online training, MARL algor…
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation
Yongsheng Mei, Guru Venkataramani, Tian Lan
Learning with multiple modalities is crucial for automated brain tumor segmentation from magnetic resonance imaging data. Explicitly optimizing the common information shared among…
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning
Hanhan Zhou, Tian Lan, Guru Venkataramani +1
One of the biggest challenges in Federated Learning (FL) is that client devices often have drastically different computation and communication resources for local updates. To this…
A Framework for Server Authentication using Communication Protocol Dialects
Kailash Gogineni, Yongsheng Mei, Guru Venkataramani +1
In today's world, computer networks have become vulnerable to numerous attacks. In both wireless and wired networks, one of the most common attacks is man-in-the-middle attacks, wi…
Popcorns-Pro: A Cooperative Network-Server Approach for Data Center Energy Optimization
Sai Santosh Dayapule, Kathy Nguyen, Gregory Kahl +2
Data centers have become a popular computing platform for various applications, and they account for nearly 2% of total US energy consumption. Therefore, it has become important to…
PT-VTON: an Image-Based Virtual Try-On Network with Progressive Pose Attention Transfer
Hanhan Zhou, Tian Lan, Guru Venkataramani
The virtual try-on system has gained great attention due to its potential to give customers a realistic, personalized product presentation in virtualized settings. In this paper, w…
MPD: Moving Target Defense through Communication Protocol Dialects
Yongsheng Mei, Kailash Gogineni, Tian Lan +1
Communication protocol security is among the most significant challenges of the Internet of Things (IoT) due to the wide variety of hardware and software technologies involved. Mov…
Integrated Reasoning Engine for Pointer-related Code Clone Detection
Hongfa Xue, Yongsheng Mei, Kailash Gogineni +2
Detecting similar code fragments, usually referred to as code clones, is an important task. In particular, code clone detection can have significant uses in the context of vulnerab…
Twin-Finder: Integrated Reasoning Engine for Pointer-related Code Clone Detection
Hongfa Xue, Yongsheng Mei, Kailash Gogineni +2
Detecting code clones is crucial in various software engineering tasks. In particular, code clone detection can have significant uses in the context of analyzing and fixing bugs in…
HolDCSim: A Holistic Simulator for Data Centers
Fan Yao, Kathy Ngyugen, Sai Santosh Dayapule +4
Cloud computing based systems, that span data centers, are commonly deployed to offer high performance for user service requests. As data centers continue to expand, computer archi…
CHOP: Bypassing Runtime Bounds Checking Through Convex Hull OPtimization
Yurong Chen, Hongfa Xue, Tian Lan +1
Unsafe memory accesses in programs written using popular programming languages like C/C++ have been among the leading causes for software vulnerability. Prior memory safety checker…
Towards a Better Indicator for Cache Timing Channels
Fan Yao, Hongyu Fang, Milos Doroslovacki +1
Recent studies highlighting the vulnerability of computer architecture to information leakage attacks have been a cause of significant concern. Among the various classes of microar…
Architecting Non-Volatile Main Memory to Guard Against Persistence-based Attacks
Fan Yao, Guru Venkataramani
DRAM-based main memory and its associated components increasingly account for a significant portion of application performance bottlenecks and power budget demands inside the compu…