5 papers
Investigating the Fundamental Limit: A Feasibility Study of Hybrid-Neural Archival
Marcus Armstrong, ZiWei Qiu, Huy Q. Vo +1
Large Language Models (LLMs) possess a theoretical capability to model information density far beyond the limits of classical statistical methods (e.g., Lempel-Ziv). However, utili…
From GPUs to RRAMs: Distributed In-Memory Primal-Dual Hybrid Gradient Method for Solving Large-Scale Linear Optimization Problem
Huynh Q. N. Vo, Md Tawsif Rahman Chowdhury, Paritosh Ramanan +4
The exponential growth of computational workloads is surpassing the capabilities of conventional architectures, which are constrained by fundamental limits. In-memory computing (IM…
Harnessing the Full Potential of RRAMs through Scalable and Distributed In-Memory Computing with Integrated Error Correction
Huynh Q. N. Vo, Md Tawsif Rahman Chowdhury, Paritosh Ramanan +2
Exponential growth in global computing demand is exacerbated due to the higher-energy requirements of conventional architectures, primarily due to energy-intensive data movement. I…
Towards Trustworthy AI: Secure Deepfake Detection using CNNs and Zero-Knowledge Proofs
H M Mohaimanul Islam, Huynh Q. N. Vo, Aditya Rane
In the era of synthetic media, deepfake manipulations pose a significant threat to information integrity. To address this challenge, we propose TrustDefender, a two-stage framework…
SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
H M Mohaimanul Islam, Huynh Q. N. Vo, Paritosh Ramanan
Stochastic optimization problems in large-scale multi-stakeholder networked systems (e.g., power grids and supply chains) rely on data-driven scenarios to encapsulate complex spati…