Journal of Advances in Developmental Research

E-ISSN: 0976-4844   •   Impact Factor: 9.71

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 1 January-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of January-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 16 Issue 1
2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Demystifying Deep Learning Compiler Optimizations for Training and Inference

Author(s) Vishakha Agrawal
Country United States
Abstract Deep learning has achieved tremendous success in recent years, powering many artificial intelligence applications. However, deep learning models are computationally intensive to train, requiring massive amounts of data and compute resources. Once trained, deep learning models need to be deployed for inference to make predictions on new data. Hardware used for training differs from hardware used for inference. Deep learning compilers have revolutionized the field of artificial intelligence by optimizing the performance of deep learning models on various hardware platforms. In the current landscape of research on deep learning compilers, there is a notable absence of comprehensive studies that specifically differentiate between compiler optimizations and methodologies for training versus inference. This paper provides detailed description of deep learning compiler optimization, focusing on training, inference separately. We investigate the challenges, opportunities, and design considerations for compilers targeting each phase.
Keywords Training, Inference, Optimization, ASIC, Fusion, Quantization, Mixed Precision, Dynamic Batching, Pruning
Published In Volume 12, Issue 2, July-December 2021
Published On 2021-09-08
Cite This Demystifying Deep Learning Compiler Optimizations for Training and Inference - Vishakha Agrawal - IJAIDR Volume 12, Issue 2, July-December 2021. DOI 10.5281/zenodo.14551855
DOI https://doi.org/10.5281/zenodo.14551855
Short DOI https://doi.org/g8wtgq
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJAIDR DOI prefix is
10.71097/IJAIDR

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 2Cover Page Vol 15 Isu 1Cover Page Vol 14 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJAIDR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijaidr.com