7 papers
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…
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…
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,…