16.2k citations · 16.4k across the 6 of their papers we have counts for
6 papers · 1 filter
OLLA: Optimizing the Lifetime and Location of Arrays to Reduce the Memory Usage of Neural Networks
Benoit Steiner, Mostafa Elhoushi, Jacob Kahn +1
The size of deep neural networks has grown exponentially in recent years. Unfortunately, hardware devices have not kept pace with the rapidly increasing memory requirements. To cop…
LoopStack: a Lightweight Tensor Algebra Compiler Stack
Bram Wasti, José Pablo Cambronero, Benoit Steiner +2
We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest. This stack ena…
Using Graph Neural Networks to model the performance of Deep Neural Networks
Shikhar Singh, Benoit Steiner, James Hegarty +1
With the unprecedented proliferation of machine learning software, there is an ever-increasing need to generate efficient code for such applications. State-of-the-art deep-learning…
Value Function Based Performance Optimization of Deep Learning Workloads
Benoit Steiner, Chris Cummins, Horace He +1
As machine learning techniques become ubiquitous, the efficiency of neural network implementations is becoming correspondingly paramount. Frameworks, such as Halide and TVM, separa…
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa +18
Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible…
Device Placement Optimization with Reinforcement Learning
Azalia Mirhoseini, Hieu Pham, Quoc V. Le +7
The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these req…