53 citations · 61 across the 7 of their papers we have counts for
8 papers
Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving
Muhammad Adnan, Rohan Mahapatra, Prashant J. Nair +4
The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges to long, compute-heavy reques…
Foresight: Adaptive Layer Reuse for Accelerated and High-Quality Text-to-Video Generation
Muhammad Adnan, Nithesh Kurella, Akhil Arunkumar +1
Diffusion Transformers (DiTs) achieve state-of-the-art results in text-to-image, text-to-video generation, and editing. However, their large model size and the quadratic cost of sp…
Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training
Muhammad Adnan, Amar Phanishayee, Janardhan Kulkarni +2
In this paper, we present a novel technique to search for hardware architectures of accelerators optimized for end-to-end training of deep neural networks (DNNs). Our approach addr…
Keyformer: KV Cache Reduction through Key Tokens Selection for Efficient Generative Inference
Muhammad Adnan, Akhil Arunkumar, Gaurav Jain +3
Transformers have emerged as the underpinning architecture for Large Language Models (LLMs). In generative language models, the inference process involves two primary phases: promp…
Accelerating Recommender Model Training by Dynamically Skipping Stale Embeddings
Yassaman Ebrahimzadeh Maboud, Muhammad Adnan, Divya Mahajan +1
Training recommendation models pose significant challenges regarding resource utilization and performance. Prior research has proposed an approach that categorizes embeddings into…
Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks
Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan +1
Recommendation models are vital in delivering personalized user experiences by leveraging the correlation between multiple input features. However, deep learning-based recommendati…
Heterogeneous Acceleration Pipeline for Recommendation System Training
Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan +1
Recommendation models rely on deep learning networks and large embedding tables, resulting in computationally and memory-intensive processes. These models are typically trained usi…
Accelerating Recommendation System Training by Leveraging Popular Choices
Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan +1
Recommender models are commonly used to suggest relevant items to a user for e-commerce and online advertisement-based applications. These models use massive embedding tables to st…