8 citations · 10 across the 12 of their papers we have counts for
12 papers
H-Spec: Parallel Speculative Decoding Without a Drafter-Side KV Cache
Weifan Jiang, Krishna Teja Chitty-Venkata, Megan Flynn +6
Speculative decoding losslessly accelerates large language model inference by having a lightweight draft model predict future tokens for verification by the target model. Recent bl…
Swimba: Switch Mamba Model Scales State Space Models
Zhixu Du, Krishna Teja Chitty-Venkata, Murali Emani +3
Mixture-of-experts (MoE) is a common approach for increasing parameter capacity, but applying MoE to state space model (SSM) token mixers can multiply the cost of the recurrent sta…
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…
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…
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…
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…
LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators
Krishna Teja Chitty-Venkata, Siddhisanket Raskar, Bharat Kale +6
Large Language Models (LLMs) have propelled groundbreaking advancements across several domains and are commonly used for text generation applications. However, the computational de…
A Survey of Techniques for Optimizing Transformer Inference
Krishna Teja Chitty-Venkata, Sparsh Mittal, Murali Emani +2
Recent years have seen a phenomenal rise in performance and applications of transformer neural networks. The family of transformer networks, including Bidirectional Encoder Represe…