-
Ansor : Generating High-Performance Tensor Programs for Deep Learning
High-performance tensor programs are crucial to guarantee efficient exec...
read it
-
How deep learning works --The geometry of deep learning
Why and how that deep learning works well on different tasks remains a m...
read it
-
JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs
The rapid evolution of deep neural networks is demanding deep learning (...
read it
-
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach
A growing number of applications implement predictive functions using de...
read it
-
SparseDNN: Fast Sparse Deep Learning Inference on CPUs
The last few years have seen gigantic leaps in algorithms and systems to...
read it
-
TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning
In this paper, we demonstrate a compiler that can optimize sparse and re...
read it
-
Using Deep Neural Networks for Estimating Loop Unrolling Factor
Optimizing programs requires deep expertise. On one hand, it is a tediou...
read it
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes. Existing deep learning systems focus on optimizing and executing static neural networks which assume a pre-determined model architecture and input data shapes–assumptions which are violated by dynamic neural networks. Therefore, executing dynamic models with deep learning systems is currently both inflexible and sub-optimal, if not impossible. Optimizing dynamic neural networks is more challenging than static neural networks; optimizations must consider all possible execution paths and tensor shapes. This paper proposes Nimble, a high-performance and flexible system to optimize, compile, and execute dynamic neural networks on multiple platforms. Nimble handles model dynamism by introducing a dynamic type system, a set of dynamism-oriented optimizations, and a light-weight virtual machine runtime. Our evaluation demonstrates that Nimble outperforms state-of-the-art deep learning frameworks and runtime systems for dynamic neural networks by up to 20x on hardware platforms including Intel CPUs, ARM CPUs, and Nvidia GPUs.
READ FULL TEXT
Comments
There are no comments yet.