Future developments in Artificial Intelligence will increasingly rely on better methods to accelerate the performance of deep learning workloads. As Deep Learning models become more complex, and as the volume of data those models are expected to handle increases rapidly, the deployment of scalable AI solutions becomes a greater challenge.

Today, two standard approaches to accelerate deep learning performance are:

  1. Design hardware solutions dedicated to deep learning computation – Many companies, ranging from startups to established manufacturers such as Intel, are actively developing Application Specific Integrated Circuits to accelerate the performance of deep learning for both training and inference.

  2. Optimize software to accelerate performance – nGraph Compiler, an open-source deep learning compiler, is Intel’s solution to deliver performance via software optimization. nGraph provides developers with a way to accelerate workloads via software and to provide a significant increase in performance for standard hardware targets such as CPUs and GPUs. For deploying scalable AI solutions, nGraph uses kernel libraries, a popular and effective method to improve deep learning performance. Where kernel libraries are available and perform well, we use them.


The current State-of-the-Art software solution for deep learning computation is to integrate kernel libraries such as Intel® Math Kernel Library for Deep Neural Networks and Nvidia’s CuDNN into deep learning frameworks. These kernel libraries offer a performance boost during runtime on specific hardware targets through highly-optimized kernels and other operator-level optimizations.

However, kernel libraries have three main problems:

  1. Kernel libraries do not support graph-level optimizations.

  2. Framework integration of kernel libraries does not scale.

  3. The number of required kernels keeps growing.

nGraph Compiler addresses the first two problems, and nGraph Compiler combined with PlaidML addresses the third problem. nGraph applies graph-level optimizations by taking the computational graph from a deep learning framework such as TensorFlow and reconstructing it with nGraph’s :abbr: IR (Intermediate Representation). nGraph IR centralizes computational graphs from various frameworks and provides a unified way to connect backends for targeted hardware. To address the third problem, nGraph is integrated with PlaidML, a tensor compiler, which generates code in LLVM, OpenCL, OpenGL, and Metal. Low-level optimizations are automatically applied to the generated code, resulting in a more efficient execution that does not require manual kernel integration for most hardware targets.

The following three sections explore the main problems of kernel libraries in more detail and describe how nGraph addresses them.

Problem 1: Kernel libraries do not support graph-level optimizations

The example diagrams below show how a deep learning framework, when integrated with a kernel library, can optimally run each operation in a computational graph, but the choice of operations in the graph may not be optimal.

Figure A: The mathematical operations in a Deep Learning stack can be simplified significantly with a graph compiler

The computation is constructed to execute (A+B)*C. With nGraph, we can further optimize the graph to be represented as A*C. From the first graph shown on the left, the operation on the constant B can be computed at compile time (an optimization known as constant folding). The graph can be further simplified to the one on the right because the constant has a value of zero (known as algebraic simplification). Without such graph-level optimizations, a deep learning framework with a kernel library will compute all operations, resulting in suboptimal execution.

Problem 2: Framework integration of kernel libraries does not scale

Due to the growing number of new deep learning accelerators, integrating kernel libraries with frameworks has become increasingly more difficult. For each new deep learning accelerator, a custom kernel library integration must be implemented by a team of experts. This labor-intensive work is further complicated by the number of frameworks, as illustrated in the following diagram.

Figure B: A many-to-many problem

Each framework must be manually integrated with each hardware-specific kernel library. Additionally, each integration is unique to the framework and its set of deep learning operators, view on memory layout, feature set, etc. Each connection that needs to be made increases the amount of work, resulting in a fragile setup that is costly to maintain.

nGraph solves this problem with bridges. A bridge takes a computational graph or similar structure and reconstructs it in the nGraph IR along with a few primitive nGraph operations. With a unified computational graph, kernel libraries no longer need to be separately integrated into each deep learning framework. Instead, the libraries only need to support nGraph primitive operations, and this approach streamlines the integration process for the backend.

Problem 3: The number of required kernels keeps growing

Integrating kernel libraries with multiple deep learning frameworks is a difficult task that becomes more complex with the growing number of kernels needed to achieve optimal performance. Past deep learning research has been built on a small set of standard computational primitives (convolution, GEMM, etc.). But as AI research advances and industrial deep learning applications continue to develop, the number of required kernels continues to increase exponentially. The number of required kernels is based on the number of chip designs, data types, operations, and the cardinality of each parameter per operation. Each connection in the following diagram represents significant work for what will ultimately be a fragile setup that is costly to maintain.

Figure C: Inevitable scaling problem

Integrating PlaidML with nGraph provides flexbility to support the latest deep learning models in the absence of hand-optimized kernels for new operations. PlaidML works together with nGraph to address the exponential growth of kernels.

PlaidML takes two inputs: the operation defined by the user and the machine description of the hardware target. It then automatically generates kernels that are iteratively optimized through an IR known as Stripe. Integration of PlaidML with nGraph allows users to choose the hardware and framework that suits their needs, resulting in freedom from kernel libraries.

Solution: nGraph and PlaidML

We developed nGraph and integrated it with PlaidML to allow developers to accelerate deep learning performance and address the problem of scalable kernel libraries. To address the problem of scaling backends, nGraph applies graph-level optimizations to deep learning computations and unifies computational graphsfrom deep learning frameworks with nGraph IR.

In conjunction with nGraph’s graph-level optimizations, PlaidML automatically applies low-level optimizations to improve deep learning performance. Additionally, PlaidML offers extensive support for various hardware targets due to its ability to generate code in LLVM, OpenCL, OpenGL, and Metal.

Given a backend with existing kernel libraries, nGraph can readily support the target hardware because the backend only needs to support a few primitive operations. If the hardware supports one of the coding languages supported by PlaidML, developers must specify the machine description to support the hardware. Together, nGraph and PlaidML provide the best of both worlds.

This documentation provides technical details of nGraph’s core functionality as well as framework and backend integrations. Creating a compiler stack like nGraph and PlaidML requires expert knowledge, and we’re confident that nGraph and PlaidML will make life easier for many kinds of developers:

  1. Framework owners looking to support new hardware and custom chips.

  2. Data scientists and ML developers wishing to accelerate deep learning performance.

  3. New DL accelerator developers creating an end-to-end software stack from a deep learning framework to their silicon.