How can systems design tools improve system performance in edge AI environments?

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Edge AI is the deployment of artificial intelligence models on edge devices, such as smartphones, cameras, sensors, or drones, that can process data locally without relying on cloud servers. Edge AI can offer benefits such as lower latency, higher privacy, and reduced bandwidth costs, but it also poses challenges for system performance, such as limited memory, power, and computing resources. How can systems design tools help you optimize your edge AI applications and overcome these constraints? In this article, you will learn about some of the tools and techniques that can help you design, test, and deploy efficient and robust edge AI systems.

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Veena Radhakrishna

1 System modeling

System modeling is the process of creating a representation of your system that captures its structure, behavior, and interactions. System modeling can help you understand the requirements, specifications, and constraints of your edge AI system, as well as identify potential bottlenecks, trade-offs, and risks. You can use system modeling tools, such as SysML, UML, or Simulink, to create diagrams, simulations, and code generation for your system. System modeling can help you validate your system design, evaluate different scenarios, and communicate your system architecture to stakeholders.

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Veena Radhakrishna
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At Cartesian, we have built an intelligent platform that enables automated robotic order fulfillment. The platform has the flexibility to handle diverse order profiles at high throughputs. The software spans multiple layers - smart bots whose load and paths are managed on the edge, which is in turn managed by orchestration and storage algorithms on the cloud. We build Digital Twins on a simulink-like tool allowing for capturing system interactions and validate the design based on what-if scenario analysis and testing. Configuration based system design allows for selection and performance analysis based on different algorithmic approaches, using the Digital Twin to visualize the effect on Order Fulfillment in the warehouse.

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2 System optimization

System optimization is the process of finding the best configuration of your system parameters and components to achieve your desired performance goals, such as accuracy, speed, energy efficiency, or reliability. System optimization can help you fine-tune your edge AI system to adapt to the specific characteristics and constraints of your edge devices and environments. You can use system optimization tools, such as MATLAB, TensorFlow Lite, or ONNX, to optimize your AI models, such as by pruning, quantization, or compression, to reduce their size and complexity. You can also use system optimization tools, such as Gurobi, CPLEX, or PuLP, to optimize your system resources, such as by scheduling, allocation, or load balancing, to improve your system throughput and utilization.

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Veena Radhakrishna
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Our system lends itself to two levels of optimisation - 1)Parameter selection for hardware setup appropriate to the customer application 2)Algorithmic optimisation for order, storage and task management based on order profile and metrics. Our proprietary order analytics tool helps us analyze large volumes of customer data and extract order and SKU profiles, key metrics and bottlenecks. This helps us select parameters for the setup. Algorithmic modules are designed to address specific challenges thrown up by the customer order and SKU profile, dividing the optimisation process into manageable components. Our proprietary simulation tools help select different algorithms, run simulations and capture key performance metrics of the system.

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3 System testing

System testing is the process of verifying that your system meets your functional and non-functional requirements and expectations. System testing can help you ensure that your edge AI system works correctly, reliably, and securely in real-world conditions. You can use system testing tools, such as PyTest, Selenium, or Appium, to automate your testing processes, such as by unit testing, integration testing, or regression testing, to detect and fix errors and bugs in your system. You can also use system testing tools, such as JMeter, Locust, or Gatling, to perform load testing, stress testing, or performance testing, to measure and improve your system scalability and resilience.

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4 System deployment

System deployment is the process of delivering your system to your target edge devices and environments. System deployment can help you distribute and update your edge AI system efficiently and securely across your network. You can use system deployment tools, such as Docker, Kubernetes, or Ansible, to create containers, orchestrate clusters, or automate tasks for your system. You can also use system deployment tools, such as AWS IoT Greengrass, Azure IoT Edge, or Google Cloud IoT Edge, to leverage cloud services, such as edge computing, edge analytics, or edge machine learning, to enhance your system functionality and flexibility.

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5 System monitoring

System monitoring is the process of collecting and analyzing data from your system to track its performance, health, and behavior. System monitoring can help you troubleshoot and resolve issues, optimize and improve your system, and learn from your system feedback and insights. You can use system monitoring tools, such as Prometheus, Grafana, or Elastic, to create dashboards, visualizations, or alerts for your system metrics, such as CPU, memory, disk, or network usage. You can also use system monitoring tools, such as Splunk, Datadog, or New Relic, to log, trace, or profile your system events, such as errors, exceptions, or anomalies.

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6 System improvement

System improvement is the process of applying changes and enhancements to your system based on your system monitoring data and feedback. System improvement can help you adapt and evolve your edge AI system to meet changing needs, demands, and opportunities. You can use system improvement tools, such as Git, GitHub, or GitLab, to manage your system versions, branches, or merges. You can also use system improvement tools, such as A/B testing, MLOps, or AutoML, to experiment with different system variants, automate your system workflows, or optimize your system parameters.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Veena Radhakrishna
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In cyber physical systems such as ours, one key consideration is architecting the split of compute across the cloud and the edge. While we had considered latency in planning this split, we had missed factoring in intermittent internet drops in a warehouse environment at a customer location. To ensure that the operating throughput is not compromised, we had to move certain decision making down from the cloud to the edge. Since the architecture is cloud native, this meant DevOps for hosting containers on the Edge. Adopting this approach upfront could have helped us plan our resources better and saved us time.

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How can systems design tools improve system performance in edge AI environments?

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1 System modeling

2 System optimization

3 System testing

4 System deployment

5 System monitoring

6 System improvement

7 Here’s what else to consider

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