research
∙
01/31/2023
Training with Mixed-Precision Floating-Point Assignments
When training deep neural networks, keeping all tensors in high precisio...
share
research
∙
01/31/2023
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters
Recent work has shown that automatic differentiation over the reals is a...
research
∙
07/28/2022
SpDISTAL: Compiling Distributed Sparse Tensor Computations
We introduce SpDISTAL, a compiler for sparse tensor algebra that targets...
research
∙
05/04/2022
Optimizing Mixture of Experts using Dynamic Recompilations
The Mixture of Experts architecture allows for outrageously large neural...
research
∙
04/19/2022
Quartz: Superoptimization of Quantum Circuits (Extended Version)
Existing quantum compilers optimize quantum circuits by applying circuit...
research
∙
03/15/2022
DISTAL: The Distributed Tensor Algebra Compiler
We introduce DISTAL, a compiler for dense tensor algebra that targets mo...
research
∙
12/10/2021
Inferring Invariants with Quantifier Alternations: Taming the Search Space Explosion
We present a PDR/IC3 algorithm for finding inductive invariants with qua...
research
∙
11/22/2021
Quanto: Optimizing Quantum Circuits with Automatic Generation of Circuit Identities
Existing quantum compilers focus on mapping a logical quantum circuit to...
research
∙
04/22/2021
Hierarchical Motion Understanding via Motion Programs
Current approaches to video analysis of human motion focus on raw pixels...
research
∙
08/15/2019
Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance
We present Task Bench, a parameterized benchmark designed to explore the...
research
∙
06/12/2019
SPoC: Search-based Pseudocode to Code
We consider the task of mapping pseudocode to long programs that are fun...
research
∙
06/09/2019
Redundancy-Free Computation Graphs for Graph Neural Networks
Graph Neural Networks (GNNs) are based on repeated aggregations of infor...
research
∙
07/14/2018
Beyond Data and Model Parallelism for Deep Neural Networks
The computational requirements for training deep neural networks (DNNs) ...
research
∙
02/14/2018
Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks
The past few years have witnessed growth in the size and computational r...
research
∙
11/09/2017
Eventually Sound Points-To Analysis with Missing Code
Static analyses make the increasingly tenuous assumption that all source...
research
∙
11/09/2017