Unlocking Embedded AI Outcomes with Lightweight Containerization

Artificial Intelligence (AI) is increasingly being leveraged at the edge, driven by the demand for faster reaction times, reduced bandwidth consumption, and enhanced data privacy. Instead of relying solely on

cloud-based processing, organizations are deploying AI directly to edge devices and systems so that data is analyzed and acted upon closer to where it's generated. 

But “the edge” isn’t a one-size-fits-all environment. Opportunities and challenges for AI deployments vary depending on whether it’s a regional edge data center, a small cluster of on-prem servers, an individual IoT gateway, or the billions of resource-constrained devices that are embedded in the physical world. Running models directly on embedded devices further amplifies the benefits of edge AI - delivering ultra-low latency, greater system resilience in intermittently connected environments, and increased privacy. Processing data immediately at the source also alleviates the need for heavier computation upstream, achieving more cost-effective strategic analytics and decision-making. 

In this blog, I’ll review use cases benefiting from embedded AI and discuss common challenges faced with these deployments. In addition, I’ll share how Atym addresses these obstacles with our solution that’s purpose-built for enabling and orchestrating containers on resource-constrained devices - even MCU based ones with as little as 256KB of memory.  

Embedded AI Use Cases We’re Seeing at Atym 

At Atym, we’ve seen significant interest in deploying AI on embedded devices across diverse industries. These implementations not only improve operational efficiency and responsiveness but also enhance security, optimize resource utilization, and drive customer satisfaction. The following are several common use cases demonstrating the transformative potential of embedded AI. 

Computer Vision 

Computer vision use cases leverage AI to interpret and analyze image data in real time AI-driven computer vision systems can accurately detect and classify objects, track movements, recognize patterns, and make instant decisions without human intervention. This capability enables improved automation, enhanced accuracy in quality assurance, proactive security responses, and personalized user experiences across all industries.  Some examples include: 

• Manufacturing Quality Control: AI embedded in camera inspection systems can enable rapid, real-time defect detection directly on production lines. This provides immediate identification and rectification of defects, drastically reducing waste and downtime, thus enhancing product quality and efficiency. 

• Security: Security cameras leveraging AI can deliver instant threat detection capabilities. Real-time processing of video feeds on-camera ensures quicker identification and response to potential threats, significantly improving safety across facilities and public spaces. This also minimizes the amount of video needing to be transmitted over costly network bandwidth to be processed centrally, which is especially important in remote locations with limited and/or expensive connectivity. 

• Retail Customer Experience: Retailers can employ AI on cameras in stores to instantly analyze customer behavior. This immediate insight helps personalize marketing strategies, enhance customer satisfaction, and optimize store layouts, all while maintaining customer privacy. 

Audio Detection 

AI-enabled audio systems can detect, classify, and respond to sounds quickly and accurately to facilitate real-time interactions, improve user experiences, elevate security standards, and optimize operational efficiency across numerous sectors. Example use cases include: 

• Hands-Free Appliance Control: Smart appliances equipped with localized voice recognition enable consumers with immediate and seamless interaction, significantly enhancing usability and reliability even without internet connectivity. On-device processing also substantially increases privacy by preventing sensitive voice data from being transmitted to cloud services, ultimately boosting consumer trust and product appeal.  

• Safety: Acoustic sensors can augment vision to provide immediate detection of critical events such as glass breaking or gun shots. By instantly identifying specific sounds, emergency services can be notified faster and more reliably, minimizing potential damage, and reducing the risk of harm to both people and assets. 

• Leak Detection: By continuously monitoring audio patterns and sound frequencies associated with fluid or gas leaks, AI-powered sensors mounted on pipes can instantly report leaks by detecting anomalies between normal and abnormal acoustic signatures. This rapid, automated response significantly reduces the risk of extensive damage and costly downtime in both residential environments and industries such as oil and gas, water utilities, and manufacturing. Further, property management companies can leverage this capability to enhance safety, operational efficiency, and proactive maintenance strategies. 

Condition Monitoring 

In addition to video and audio, embedded AI can also be used to process sensor-generated telemetry data such as location, temperature, pressure, vibration, and more.  AI-powered condition monitoring supports improved operations and proactive maintenance strategies across industries reliant on heavy machinery and critical infrastructure. This immediate analysis facilitates real-time diagnostics, predictive insights, and automated responses, ensuring early detection of potential failures, minimizing downtime, reducing maintenance costs, and significantly enhancing overall operational efficiency and reliability. 

• Predictive Maintenance: Embedded AI supports predictive maintenance of machines by continuously analyzing sensor data such as temperature, pressure, voltage, and current to enable rapid identification of operational anomalies. As with video and audio, on-device AI is especially useful for high-bandwidth sensor data such as vibration signals that would otherwise consume valuable network bandwidth if streamed to a central location for processing. This advanced analysis enables AI systems to predict potential equipment failures well before they occur, allowing organizations to schedule maintenance proactively rather than reactively. By anticipating issues early, industries such as manufacturing, transportation, energy, and mining experience significant reductions in unplanned downtime, lower maintenance costs, improved equipment reliability, and prolonged asset lifespan.  

• Remote Condition Monitoring: Embedded AI is an ideal approach for enhancing real-time monitoring of remote assets and operations such as EV chargers, solar farms, oil fields, mines, and farms that are connected to the cloud via constrained and/or expensive networks such as LoRa and satellite. Localized analytics provide fast reaction times regardless of connectivity status and conserve valuable network bandwidth because only meaningful events need to be backhauled instead of raw data. Lightweight AI models also enable operators to utilize lower cost devices that can be more widely distributed to get a better “pulse” of the overall environment and rapidly identify and act on operational anomalies.   

Challenges in Implementing Embedded AI Use Cases 

Deploying AI on embedded devices instead of more capable edge systems involves several significant challenges though. Embedded devices typically have constrained processing power, memory, and storage – limiting the size of AI models and preventing the use of software abstraction tools such as Docker and Kubernetes that are common further up the stack.  These resource-constrained devices have traditionally been powered by either embedded Linux, or in the case of MCUs, firmware that’s rigid to change and innovation.    

These monolithic programming environments greatly complicate the development, integration, and management of AI models.  For starters, a developer skilled in building AI models is typically not familiar with working in lower-level, C/C++ device code. Working in firmware also requires IP to be shared as source code, introducing risk when multiple vendors or third parties are involved. 

Further, with firmware (and to some degree embedded Linux), any single change requires the entire code base to be recompiled, retested, and redeployed.  With AI models needing to be updated as they evolve, this introduces extra risk and downtime for embedded devices because they need to be rebooted and can be bricked if power is lost in the process. Sending full image updates over the wire also consumes valuable network bandwidth at remote sites.   

How Atym Solves Embedded AI Challenges 

Atym addresses these challenges by offering a container orchestration solution for resource-constrained edge devices. Leveraging WebAssembly (Wasm), Atym enables containerization on CPU and MCU-based devices with as little as 256KB of memory, bringing the agility of cloud-native development practices to the embedded world. The solution provides a very similar experience to Docker but with a 2000x lighter footprint. 

By turning firmware and embedded Linux into containerized software, Atym enables AI models and other functions to be written in different programming languages and deployed, updated, and managed independently. Atym’s benefits specific to embedded AI include: 

• Decoupling AI Models from Other Apps and Underlying Hardware: Atym enables containers to independently run within secure, sandboxed environments. By decoupling applications from each other and the underlying infrastructure with containers, Atym empowers developers to rapidly build AI models and other applications on independent timelines and without being tied to specific hardware architectures.  This helps accelerate project schedules and enables portability of code across different products.  Containerized AI models can also be updated and optimized frequently without disrupting other embedded software, significantly enhancing agility and reducing overall maintenance complexity.  

• Separate AI Workflows from Embedded Development: Atym empowers embedded and AI developers to work independently by abstracting hardware complexity through containerization. Embedded developers can continue working in C/C++ to implement low-level drivers, while AI developers can use familiar tools like Edge Impulse, TensorFlow, and PyTorch to develop and deploy models. This separation allows both teams to maintain their own familiar workflows and iterate rapidly without interfering with each other, reducing integration overhead and accelerating time to deployment.  

• Fractional Updates without Rebooting: Fractional updates allow updates of AI models on edge devices independently of other existing code. Portions of the AI model can also be containerized – for example, the model weights - further reducing update sizes. Updates occur without rebooting, enabling continuous operation with no forced downtime.  

• Reduced Data Required for Updates: The overhead of Atym’s lightweight containers (typically in single KBs) is significantly lower than the overhead of Docker containers (typically 10sMB+). This efficiency enables AI models to be deployed on more constrained hardware and reduces the amount of data transmitted for updates, making it ideal for applications with limited bandwidth connectivity in remote locations for use cases in energy, farming, mining, and the like. Operators can efficiently update devices without overburdening network resources while saving costs. 

• Protecting Intellectual Property and Enabling BYOAI: Atym’s secure, containerized deployment encapsulates proprietary models and algorithms as binaries, safeguarding sensitive IP when collaborating with third parties. Whether it’s to integrate IP from a partner or enable an end customer to deploy their own AI models on devices (“BYOAI”), Atym containerization facilitates more secure collaboration and innovation as well as broader business opportunities. 

• Partnerships: Because Atym is not in the data path and is cloud-agnostic, our customers are free to mix and match preferred technologies and solutions in their deployments without worrying about overlap or competing integrations. Atym is partnered with leading providers of AI development tools, data platforms, and hardware so you can choose the right fit for your application.    

Atym containerization enables embedded AI code to be developed and updated independently of other apps

In Conclusion 

As more industries turn to AI for competitive advantage, deploying intelligence on embedded devices and systems – immediately proximal to the source of data - is increasingly becoming essential. However, embedded environments present unique challenges. 

At Atym, we’re unlocking the power of embedded AI with our lightweight, secure, and flexible container orchestration solution that’s purpose-built for resource constrained devices. Our platform enables organizations to operationalize AI on embedded devices with the flexibility, security, and performance they need—without the overhead of traditional container solutions. Whether you're building a computer vision system, audio detection device, or condition monitoring solution, Atym makes it simple to deploy, update, and manage AI models at scale 

For more insights on how Atym helps with AI model implementation, check out our joint webinar with Edge Impulse on ”Integrating Embedded AI on Edge Devices at Scale” or contact us to learn more.