PLDI 2024
Mon 24 - Fri 28 June 2024 Copenhagen, Denmark
Mon 24 Jun 2024 12:05 - 12:20 at Iceland - Optimization Chair(s): Aviral Shrivastava

Arising popularity for resource-efficient machine learning models makes random forest and decision trees famous models in recent years. Naturally, these models are tuned, optimized, and transformed to feature maximally low resource consumption. A subset of these strategies targets the model structure and model logic and therefore induces a trade-off between resource efficiency and prediction performance. An orthogonal set of approaches targets hardware-specific optimizations, which can improve performance without changing the behavior of the model. Since such hardware-specific optimizations are usually hardware-dependent and inflexible in their realizations, this paper envisions a more general application of such optimization strategies at the level of programming languages. We therefore discuss a set of suitable optimization strategies first in general and envision their application in LLVM IR, i.e. a flexible and hardware independent ecosystem.

Mon 24 Jun

Displayed time zone: Windhoek change

10:40 - 12:20
OptimizationLCTES at Iceland
Chair(s): Aviral Shrivastava Arizona State University
10:40
15m
Talk
Accelerating Shared Library Execution in a DBT
LCTES
Tom Spink University of St Andrews, Björn Franke University of Edinburgh
10:55
15m
Talk
Efficient Implementation of Neural Networks Usual Layers on Fixed-Point Architectures
LCTES
Dorra Ben Khalifa University of Toulouse - ENAC, Matthieu Martel Université de Perpignan Via Domitia
11:10
15m
Talk
TinySeg: Model Optimizing Framework for Image Segmentation on Tiny Embedded Systems
LCTES
Byungchul Chae Kyung Hee University, Jiae Kim Kyung Hee University, Seonyeong Heo Kyung Hee University
11:25
10m
Break
Break - 10 minutes
LCTES

11:35
15m
Talk
MixPert: Optimizing Mixed-Precision Floating-Point Emulation on GPU Integer Tensor Cores
LCTES
Zejia Lin Sun Yat-sen University, Aoyuan Sun Sun Yat-sen University, Xianwei Zhang Sun Yat-sen University, Yutong Lu Sun Yat-sen University
11:50
15m
Talk
Optimistic and Scalable Global Function Merging
LCTES
12:05
15m
Talk
(Invited paper) Language-Based Deployment Optimization for Random Forest
LCTES
Jannik Malcher TU Dortmund University, Daniel Biebert TU Dortmund University, Kuan-Hsun Chen University of Twente, Sebastian Buschjäger TU Dortmund University, Christian Hakert TU Dortmund University, Jian-Jia Chen TU Dortmund University