Search Results for author:
Found 11 papers, 5 papers with code
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs
We first characterize the proportion of data to sample from each region of a program's input space (corresponding to different execution paths of the program) based on the complexity of learning a surrogate of the corresponding execution path.
COMET: Neural Cost Model Explanation Framework
We generate and compare COMET's explanations for the popular neural cost model, Ithemal against those for an accurate CPU simulation-based cost model, uiCA.
The Effect of Data Dimensionality on Neural Network Prunability
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 model's test accuracy.
Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression
Our evaluation shows that compared to the state-of-the-art SEML approach in computer systems optimization, Cello improves latency by 1. 19X for minimizing latency under a power constraint, and improves energy by 1. 18X for minimizing energy under a latency constraint.
Programming with Neural Surrogates of Programs
With surrogate adaptation, programmers develop a surrogate of a program then retrain that surrogate on a different task.
Fast Binarized Neural Network Training with Partial Pre-training
Binarized neural networks, networks with weights and activations constrained to lie in a 2-element set, allow for more time- and resource-efficient inference than standard floating-point networks.
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates
In this paper we present DiffTune, a system for learning the parameters of x86 basic block CPU simulators from coarse-grained end-to-end measurements.
TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning
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 accelerated with sparse tensor algebra techniques).
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Learning rate rewinding (which we propose) trains the unpruned weights from their final values using the same learning rate schedule as weight rewinding.
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady state (the throughput) is important for both compiler designers and performance engineers.
Abstractions for AI-Based User Interfaces and Systems
We propose to treat these challenges as language-design problems.
