180 citations · 196 across the 10 of their papers we have counts for
10 papers
Learning to Compile Programs to Neural Networks
Logan Weber, Jesse Michel, Alex Renda +1
A is a neural network that mimics the behavior of a program. Researchers have used these neural surrogates to automatically tune program in…
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs
Alex Renda, Yi Ding, Michael Carbin
Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software dev…
COMET: Neural Cost Model Explanation Framework
Isha Chaudhary, Alex Renda, Charith Mendis +1
Cost models predict the cost of executing given assembly code basic blocks on a specific microarchitecture. Recently, neural cost models have been shown to be fairly accurate and e…
The Effect of Data Dimensionality on Neural Network Prunability
Zachary Ankner, Alex Renda, Gintare Karolina Dziugaite +2
Practitioners prune neural networks for efficiency gains and generalization improvements, but few scrutinize the factors determining the prunability of a neural network the maximum…
Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression
Yi Ding, Alex Renda, Ahsan Pervaiz +2
Sample-efficient machine learning (SEML) has been widely applied to find optimal latency and power tradeoffs for configurable computer systems. Instead of randomly sampling from th…
Programming with Neural Surrogates of Programs
Alex Renda, Yi Ding, Michael Carbin
Surrogates, models that mimic the behavior of programs, form the basis of a variety of development workflows. We study three surrogate-based design patterns, evaluating each in cas…