Alex Renda

According to our database, Alex Renda authored at least 13 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

COMET: Neural Cost Model Explanation Framework.

Isha Chaudhary, Alex Renda, Charith Mendis, Gagandeep Singh

Proceedings of the Seventh Annual Conference on Machine Learning and Systems, 2024

Learning to Compile Programs to Neural Networks.

Logan Weber, Jesse Michel, Alex Renda, Michael Carbin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs.

Alex Renda, Yi Ding, Michael Carbin

Proc. ACM Program. Lang., October, 2023

CoMEt: x86 Cost Model Explanation Framework.

Isha Chaudhary, Alex Renda, Charith Mendis, Gagandeep Singh

CoRR, 2023

2022

The Effect of Data Dimensionality on Neural Network Prunability.

Zachary Ankner, Alex Renda, Gintare Karolina Dziugaite, Jonathan Frankle, Tian Jin

CoRR, 2022

Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression.

Yi Ding, Alex Renda, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann

CoRR, 2022

2021

Programming with neural surrogates of programs.

Alex Renda, Yi Ding, Michael Carbin

Proceedings of the Onward! 2021: Proceedings of the 2021 ACM SIGPLAN International Symposium on New Ideas, 2021

2020

TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning.

Riyadh Baghdadi, Abdelkader Nadir Debbagh, Kamel Abdous, Fatima-Zohra Benhamida, Alex Renda, Jonathan Elliott Frankle, Michael Carbin, Saman P. Amarasinghe

CoRR, 2020

DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates.

Alex Renda, Yishen Chen, Charith Mendis, Michael Carbin

Proceedings of the 53rd Annual IEEE/ACM International Symposium on Microarchitecture, 2020

Comparing Rewinding and Fine-tuning in Neural Network Pruning.

Alex Renda, Jonathan Frankle, Michael Carbin

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

BHive: A Benchmark Suite and Measurement Framework for Validating x86-64 Basic Block Performance Models.

Yishen Chen, Ajay Brahmakshatriya, Charith Mendis, Alex Renda, Eric Atkinson, Ondrej Sýkora, Saman P. Amarasinghe, Michael Carbin

Proceedings of the IEEE International Symposium on Workload Characterization, 2019

Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks.

Charith Mendis, Alex Renda, Saman P. Amarasinghe, Michael Carbin

Proceedings of the 36th International Conference on Machine Learning, 2019

2017

Abstractions for AI-Based User Interfaces and Systems.

Alex Renda, Harrison Goldstein, Sarah Bird, Chris Quirk, Adrian Sampson

CoRR, 2017