92 citations · 237 across the 29 of their papers we have counts for
13 papers · 1 filter
AI Benchmark Democratization and Carpentry
Gregor von Laszewski, Wesley Brewer, Jeyan Thiyagalingam +28
Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring d…
ImageNet-Think-250K: A Large-Scale Synthetic Dataset for Multimodal Reasoning for Vision Language Models
Krishna Teja Chitty-Venkata, Murali Emani
We develop ImageNet-Think, a multimodal reasoning dataset designed to aid the development of Vision Language Models (VLMs) with explicit reasoning capabilities. Our dataset is buil…
Foundation Models for Discovery and Exploration in Chemical Space
Alexius Wadell, Anoushka Bhutani, Victor Azumah +26
Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches…
AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions
Väinö Hatanpää, Eugene Ku, Jason Stock +12
Generative machine learning offers new opportunities to better understand complex Earth system dynamics. Recent diffusion-based methods address spectral biases and improve ensemble…
PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference
Krishna Teja Chitty-Venkata, Jie Ye, Xian-He Sun +4
KV caching significantly improves the efficiency of Large Language Model (LLM) inference by storing attention states from previously processed tokens, enabling faster generation of…
LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference
Krishna Teja Chitty-Venkata, Sandeep Madireddy, Murali Emani +1
Mixture-of-Experts (MoE) models scale efficiently by activating only a subset of experts per token, offering a computationally sparse alternative to dense architectures. While prio…
MoPEQ: Mixture of Mixed Precision Quantized Experts
Krishna Teja Chitty-Venkata, Jie Ye, Murali Emani
Large Language and Vision Models using a Mixture-of-Experts (MoE) architecture pose significant challenges for deployment due to their computational and memory demands. Mixed Preci…
PreLoRA: Hybrid Pre-training of Vision Transformers with Full Training and Low-Rank Adapters
Krishu K Thapa, Reet Barik, Krishna Teja Chitty-Venkata +2
Training large models ranging from millions to billions of parameters is highly resource-intensive, requiring significant time, compute, and memory. It is observed that most of the…
Aurora: Architecting Argonne's First Exascale Supercomputer for Accelerated Scientific Discovery
William E. Allcock, Benjamin S. Allen, James Anchell +106
Aurora is Argonne National Laboratory's pioneering Exascale supercomputer, designed to accelerate scientific discovery with cutting-edge architectural innovations. Key new technolo…
MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models
Krishna Teja Chitty-Venkata, Sylvia Howland, Golara Azar +5
Mixture of Experts (MoE) models have enabled the scaling of Large Language Models (LLMs) and Vision Language Models (VLMs) by achieving massive parameter counts while maintaining c…
LangVision-LoRA-NAS: Neural Architecture Search for Variable LoRA Rank in Vision Language Models
Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath
Vision Language Models (VLMs) integrate visual and text modalities to enable multimodal understanding and generation. These models typically combine a Vision Transformer (ViT) as a…
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…
BaKlaVa -- Budgeted Allocation of KV cache for Long-context Inference
Ahmed Burak Gulhan, Krishna Teja Chitty-Venkata, Murali Emani +2
In Large Language Model (LLM) inference, Key-Value (KV) caches (KV-caches) are essential for reducing time complexity. However, they result in a linear increase in GPU memory as th…