Divya Mahajan

Orcid: 0009-0007-8184-0528

Affiliations:
  • Microsoft, Redmond, WA, USA
  • Georgia Institute of Technology, GA, USA (Ph.D.)
  • Texas State University, TX, USA (former)


According to our database1, Divya Mahajan authored at least 28 papers between 2014 and 2024.

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Timeline

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Bibliography

2024
Accelerating String-key Learned Index Structures via Memoization-based Incremental Training.
Proc. VLDB Endow., April, 2024

Data-driven Forecasting of Deep Learning Performance on GPUs.
CoRR, 2024

Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training.
CoRR, 2024

Accelerating Recommender Model Training by Dynamically Skipping Stale Embeddings.
CoRR, 2024

Heterogeneous Acceleration Pipeline for Recommendation System Training.
Proceedings of the 51st ACM/IEEE Annual International Symposium on Computer Architecture, 2024

Integrated Hardware Architecture and Device Placement Search.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

NeuPIMs: NPU-PIM Heterogeneous Acceleration for Batched LLM Inferencing.
Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024

2023
Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks.
CoRR, 2023

FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Yin-Yang: Programming Abstractions for Cross-Domain Multi-Acceleration.
IEEE Micro, 2022

2021
Accelerating Recommendation System Training by Leveraging Popular Choices.
Proc. VLDB Endow., 2021

High-Performance Training by Exploiting Hot-Embeddings in Recommendation Systems.
CoRR, 2021

A Computational Stack for Cross-Domain Acceleration.
Proceedings of the IEEE International Symposium on High-Performance Computer Architecture, 2021

2020
Efficient Algorithms for Device Placement of DNN Graph Operators.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Balancing Generality and Specialization for Machine Learning in the Post-ISA Era.
PhD thesis, 2019

Improving the energy efficiency of relational and NoSQL databases via query optimizations.
Sustain. Comput. Informatics Syst., 2019

2018
In-RDBMS Hardware Acceleration of Advanced Analytics.
Proc. VLDB Endow., 2018

RoboX: An End-to-End Solution to Accelerate Autonomous Control in Robotics.
Proceedings of the 45th ACM/IEEE Annual International Symposium on Computer Architecture, 2018

2017
AxBench: A Multiplatform Benchmark Suite for Approximate Computing.
IEEE Des. Test, 2017

Scale-out acceleration for machine learning.
Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture, 2017

Energy efficiency analysis of query optimizations on MongoDB and Cassandra.
Proceedings of the Eighth International Green and Sustainable Computing Conference, 2017

2016
From high-level deep neural models to FPGAs.
Proceedings of the 49th Annual IEEE/ACM International Symposium on Microarchitecture, 2016

Towards Statistical Guarantees in Controlling Quality Tradeoffs for Approximate Acceleration.
Proceedings of the 43rd ACM/IEEE Annual International Symposium on Computer Architecture, 2016

TABLA: A unified template-based framework for accelerating statistical machine learning.
Proceedings of the 2016 IEEE International Symposium on High Performance Computer Architecture, 2016

AxGames: Towards Crowdsourcing Quality Target Determination in Approximate Computing.
Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems, 2016

2015
Axilog: Abstractions for Approximate Hardware Design and Reuse.
IEEE Micro, 2015

Axilog: language support for approximate hardware design.
Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition, 2015

2014
Memristor based adders.
Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014


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