What kinds of tools are available for optimizing edge AI performance?

Developers can turn to model optimization frameworks and libraries, model optimization, distillation, and compression tools, plus hardware-specific optimization tools and development platforms for optimizing edge AI performance. Kubernetes and containerization provide additional tools for optimizing AI edge performance.

Frameworks and libraries are collections of pre-written code. Libraries are collections of components, classes, and methods that developers can use to implement specific functions. Frameworks provide code that already performs specific functions.

Libraries offer flexibility and control but require more manual effort in structuring code. Frameworks, on the other hand, can offer structure and a more complete solution but may limit flexibility. Frameworks can include libraries (Figure 1).

Figure 1. Frameworks call code, and code can call a library either from within a framework or independently. (Image: TheServerSide)

The specific requirements, complexity, and the desired level of control and flexibility for specific projects guide the choice between a library or framework approach. However, frameworks and libraries are not mutually exclusive and can be combined in a single project to leverage their respective strengths.

Development tools

A few examples of the numerous edge AI development tools include:

TensorFlow Lite is optimized for on-device inference on mobile and embedded devices. It includes tools for model conversion and optimization, including quantization and pruning.
ONNX Runtime is an open-source inference engine that enables efficient execution of models from various frameworks across multiple hardware environments.
Apache TVM is a deep learning compiler that can optimize models for various hardware systems, including CPUs, GPUs, and custom accelerators.
Edge Impulse supports data acquisition, signal processing, ML training, and model testing for creating and deploying ML on edge devices.

Optimization tools and techniques

Effective ways to optimize edge AI performance involve reducing model size and computational demands while maintaining the needed level of accuracy. Examples of optimization tools and techniques include:

Model quantization reduces the precision of model weights, like from 32-bit floating point to 8-bit integer, significantly decreasing model size and computational requirements while maintaining the required accuracy.

Pruning removes redundant or relatively unimportant connections and neurons, reducing the size of the model and speeding inference times.

Knowledge distillation produces a smaller model, sometimes called a “student” model, that mimics the performance of the original and more accurate “teacher” model. Properly implemented, this can significantly reduce model complexity and preserve needed accuracy.

Kubernetes

Kubernetes is an open-source platform for running applications in containers that offer advantages compared to virtual machines. Virtualization allows applications to be isolated between VMs and provides a level of security, as the information of one application cannot be freely accessed by another application.

Containers are lightweight solutions and have relaxed isolation properties for sharing the OS between applications. Kubernetes streamlines the process of deploying, managing, and scaling containerized applications (Figure 2).

Figure 2. Kubernetes replaces virtualization and is used to manage lightweight solutions based on containers. (Image: Akamai)

Lightweight Kubernetes distributions like KubeEdge and K3s can be especially useful for edge environments. They can support automated workload deployment, scaling, and maintenance with the consistency usually associated with cloud data centers.

In edge deployments that are potentially unstable, Kubernetes ensures application resilience with mechanisms like automatically rescheduling failed containers. Its ability to support multi-node clusters supports redundancy to maintain system integrity and service availability in the event of a service failure.

Using autoscaling, Kubernetes can dynamically allocate resources to applications based on current demand, preventing over-provisioning or resource exhaustion. That, in turn, supports scalability in edge computing environments.

Scalability can also be implemented horizontally by adding more instances instead of simply adding capacity to existing services. That supports improved performance and enhances redundancy by distributing workloads.

Kubernetes has multiple security layers, including network policies, role-based access control (RBAC), and secrets management, ensuring that edge applications are protected from unauthorized access and data breaches.

Summary

Frameworks and libraries are useful tools for edge AI development. Developers can turn to model quantization, pruning, and knowledge distillation when optimizing edge AI models. Kubernetes can be used to replace virtualization and manage lightweight solutions based on containers.