12 papers
ARMOR-IMC: Adaptive Resource Mapping for Operational Robustness via Secure In-Memory Computing
Muhtasim Alam Chowdhury, Ramtin Zand, Soheil Salehi
The massive data-movement overhead in traditional architectures has led to the adoption of In-Memory Computing (IMC) for energy-efficient Deep Neural Network (DNN) processing. By l…
LIMCA: LLM for Automating Analog In-Memory Computing Architecture Design Exploration
Deepak Vungarala, Md Hasibul Amin, Pietro Mercati +5
Resistive crossbars enabling analog In-Memory Computing (IMC) have emerged as a promising architecture for Deep Neural Network (DNN) acceleration, offering high memory bandwidth an…
Rep Smarter, Not Harder: AI Hypertrophy Coaching with Wearable Sensors and Edge Neural Networks
Grant King, Musa Azeem, Savannah Noblitt +2
Optimizing resistance training for hypertrophy requires balancing proximity to muscular failure, often quantified by Repetitions in Reserve (RiR), with fatigue management. However,…
FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design
Mahmoud Nazzal, Khoa Nguyen, Deepak Vungarala +4
AI hardware design is advancing rapidly, driven by the promise of design automation to make chip development faster, more efficient, and more accessible to a wide range of users. A…
CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems
Md Hasibul Amin, Mohammadreza Mohammadi, Jason D. Bakos +1
In this paper, we propose the CrossNAS framework, an automated approach for exploring a vast, multidimensional search space that spans various design abstraction layers-circuits, a…
NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines
Chathurangi Shyalika, Renjith Prasad, Fadi El Kalach +4
In modern assembly pipelines, identifying anomalies is crucial in ensuring product quality and operational efficiency. Conventional single-modality methods fail to capture the intr…