78 citations · 322 across the 17 of their papers we have counts for
19 papers
LMS-AR: LMS Prediction-based Adaptive Regulator for Memory Bandwidth in Multicore Systems
Sudarshan Srinivasan, Deepak Gangadharan, Dip Goswami
Memory bandwidth contention in multi-core systems severely impacts application performance and quality-of-service (QoS) guarantees. Regulating the shared memory bandwidth mitigates…
PCCL: Process Group-Aware Scalable and Generic Collective Algorithm Synthesizer
William Won, Kartik Lakhotia, Madhu Kumar +2
Distributed machine learning has become increasingly important due to the massive scale of large-scale generative models. Both model parameters and data are distributed across many…
How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving
Hanjiang Wu, Abhimanyu Rajeshkumar Bambhaniya, Sarbartha Banerjee +9
Modern large language model (LLM) inference has progressively disaggregated to keep pace with growing model sizes and tight TTFT and TPOT service-level objectives: from chunked-pre…
MIST: A Co-Design Framework for Heterogeneous, Multi-Stage LLM Inference
Abhimanyu Rajeshkumar Bambhaniya, Hanjiang Wu, Suvinay Subramanian +8
Modern LLM serving now spans multi-stage pipelines including RAG retrieval and KV cache reuse, each with distinct compute, memory, and latency demands. Inference engines expose a l…
FRED: Flexible REduction-Distribution Interconnect and Communication Implementation for Wafer-Scale Distributed Training of DNN Models
Saeed Rashidi, William Won, Sudarshan Srinivasan +2
Distributed Deep Neural Network (DNN) training is a technique to reduce the training overhead by distributing the training tasks into multiple accelerators, according to a parallel…
Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models
Abhimanyu Bambhaniya, Ritik Raj, Geonhwa Jeong +6
Large language models (LLMs) have shown remarkable performance across a wide range of applications, often outperforming human experts. However, deploying these gigantic models effi…
TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Machine Learning
William Won, Midhilesh Elavazhagan, Sudarshan Srinivasan +2
The surge of artificial intelligence, particularly large language models, has driven the rapid development of large-scale machine learning clusters. Executing distributed models on…
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale
William Won, Taekyung Heo, Saeed Rashidi +3
As deep learning models and input data are scaling at an unprecedented rate, it is inevitable to move towards distributed training platforms to fit the model and increase training…
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task
Maria Mahbub, Sudarshan Srinivasan, Edmon Begoli +1
Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them.…
Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models
Saeed Rashidi, William Won, Sudarshan Srinivasan +2
Distributed training is a solution to reduce DNN training time by splitting the task across multiple NPUs (e.g., GPU/TPU). However, distributed training adds communication overhead…
LIBRA: Enabling Workload-aware Multi-dimensional Network Topology Optimization for Distributed Training of Large AI Models
William Won, Saeed Rashidi, Sudarshan Srinivasan +1
As model sizes in machine learning continue to scale, distributed training is necessary to accommodate model weights within each device and to reduce training time. However, this c…
Extending Sparse Tensor Accelerators to Support Multiple Compression Formats
Eric Qin, Geonhwa Jeong, William Won +7
Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential mem…
The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations
Jeremiah Duncan, Fabian Fallas, Chris Gropp +11
Authorship analysis is an important subject in the field of natural language processing. It allows the detection of the most likely writer of articles, news, books, or messages. Th…
Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms
Saeed Rashidi, Matthew Denton, Srinivas Sridharan +4
Deep Learning (DL) training platforms are built by interconnecting multiple DL accelerators (e.g., GPU/TPU) via fast, customized interconnects with 100s of gigabytes (GBs) of bandw…
Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures
Dhiraj Kalamkar, Evangelos Georganas, Sudarshan Srinivasan +3
During the last two years, the goal of many researchers has been to squeeze the last bit of performance out of HPC system for AI tasks. Often this discussion is held in the context…
K-TanH: Efficient TanH For Deep Learning
Abhisek Kundu, Alex Heinecke, Dhiraj Kalamkar +7
We propose K-TanH, a novel, highly accurate, hardware efficient approximation of popular activation function TanH for Deep Learning. K-TanH consists of parameterized low-precision…
High Performance Scalable FPGA Accelerator for Deep Neural Networks
Sudarshan Srinivasan, Pradeep Janedula, Saurabh Dhoble +7
Low-precision is the first order knob for achieving higher Artificial Intelligence Operations (AI-TOPS). However the algorithmic space for sub-8-bit precision compute is diverse, w…
A Study of BFLOAT16 for Deep Learning Training
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi +16
This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training acros…
Mixed Precision Training With 8-bit Floating Point
Naveen Mellempudi, Sudarshan Srinivasan, Dipankar Das +1
Reduced precision computation for deep neural networks is one of the key areas addressing the widening compute gap driven by an exponential growth in model size. In recent years, d…