8 papers
TraceNAS: Zero-shot LLM Pruning via Gradient Trace Correlation
Prajna G. Malettira, Manish Nagaraj, Arjun Roy +2
Structured pruning is essential for efficient deployment of Large Language Models (LLMs). The varying sensitivity of LLM sub-blocks to pruning necessitates the identification of op…
TopoPrune: Robust Data Pruning via Unified Latent Space Topology
Arjun Roy, Prajna G. Malettira, Manish Nagaraj +1
Geometric data pruning methods, while practical for leveraging pretrained models, are fundamentally unstable. Their reliance on extrinsic geometry renders them highly sensitive to…
LIMO: Low-Power In-Memory-Annealer and Matrix-Multiplication Primitive for Edge Computing
Amod Holla, Sumedh Chatterjee, Sutanu Sen +5
Combinatorial optimization (CO) underpins applications in science and engineering, ranging from logistics to electronic design automation. A classic example is the NP-complete Trav…
AgriRegion: Region-Aware Retrieval for High-Fidelity Agricultural Advice
Mesafint Fanuel, Mahmoud Nabil Mahmoud, Crystal Cook Marshal +4
Large Language Models (LLMs) have demonstrated significant potential in democratizing access to information. However, in the domain of agriculture, general-purpose models frequentl…
HALO: Memory-Centric Heterogeneous Accelerator with 2.5D Integration for Low-Batch LLM Inference
Shubham Negi, Kaushik Roy
The rapid adoption of Large Language Models (LLMs) has driven a growing demand for efficient inference, particularly in latency-sensitive applications such as chatbots and personal…
COMET: A Framework for Modeling Compound Operation Dataflows with Explicit Collectives
Shubham Negi, Manik Singhal, Aayush Ankit +2
Modern machine learning accelerators are designed to efficiently execute deep neural networks (DNNs) by optimizing data movement, memory hierarchy, and compute throughput. However,…
Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems
Aniruddha Chattopadhyay, Raj Dandekar, Kaushik Roy
Neurosymbolic artificial intelligence (AI) systems combine neural network and classical symbolic AI mechanisms to exploit the complementary strengths of large scale, generalizable…
SHA-CNN: Scalable Hierarchical Aware Convolutional Neural Network for Edge AI
Narendra Singh Dhakad, Yuvnish Malhotra, Santosh Kumar Vishvakarma +1
This paper introduces a Scalable Hierarchical Aware Convolutional Neural Network (SHA-CNN) model architecture for Edge AI applications. The proposed hierarchical CNN model is metic…