Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Renda, Alex"'
Autor:
Renda, Alex
Surrogate programming, the act of replacing programs with surrogate models of their behavior, is being increasingly leveraged to solve software development challenges. Surrogates are typically machine learning models trained on input-output examples
Externí odkaz:
https://hdl.handle.net/1721.1/156602
A $\textit{neural surrogate of a program}$ is a neural network that mimics the behavior of a program. Researchers have used these neural surrogates to automatically tune program inputs, adapt programs to new settings, and accelerate computations. Res
Externí odkaz:
http://arxiv.org/abs/2407.15078
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 development challenges. Programmers train surrogates from measurements of
Externí odkaz:
http://arxiv.org/abs/2309.11726
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 easy to construct. They can replace heavily engineered analytical cost
Externí odkaz:
http://arxiv.org/abs/2302.06836
Practitioners prune neural networks for efficiency gains and generalization improvements, but few scrutinize the factors determining the prunability of a neural network the maximum fraction of weights that pruning can remove without compromising the
Externí odkaz:
http://arxiv.org/abs/2212.00291
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 the configuration space, SEML reduces the search cost by dramatically re
Externí odkaz:
http://arxiv.org/abs/2204.04831
Publikováno v:
Onward! 2021
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 case studies on a large-scale CPU simulator. With surrogate compilation,
Externí odkaz:
http://arxiv.org/abs/2112.06148
Publikováno v:
MICRO 2020
CPU simulators are useful tools for modeling CPU execution behavior. However, they suffer from inaccuracies due to the cost and complexity of setting their fine-grained parameters, such as the latencies of individual instructions. This complexity ari
Externí odkaz:
http://arxiv.org/abs/2010.04017
Autor:
Baghdadi, Riyadh, Debbagh, Abdelkader Nadir, Abdous, Kamel, Benhamida, Fatima Zohra, Renda, Alex, Frankle, Jonathan Elliott, Carbin, Michael, Amarasinghe, Saman
In this paper, we demonstrate a compiler that can optimize sparse and recurrent neural networks, both of which are currently outside of the scope of existing neural network compilers (sparse neural networks here stand for networks that can be acceler
Externí odkaz:
http://arxiv.org/abs/2005.04091
Publikováno v:
ICLR 2020
Many neural network pruning algorithms proceed in three steps: train the network to completion, remove unwanted structure to compress the network, and retrain the remaining structure to recover lost accuracy. The standard retraining technique, fine-t
Externí odkaz:
http://arxiv.org/abs/2003.02389