The Challenges of Deploying AI to the Edge

June 17, 2024

Artificial intelligence (AI) is in the midst of rapid, seemingly exponential advancements. However, most of what is seen in the news and read in articles deals with the consumer side of the technology. While AI technology is also rapidly improving in industrial settings, some significant challenges remain, specifically in AI edge computing applications.

The issues presented by AI edge computing can be categorized into hardware limitations, software and algorithmic challenges, security and privacy concerns, and deployment and maintenance difficulties.

Hardware Limitations

Edge devices typically have limited computational resources, especially when compared to cloud-based servers. Running complex AI models and/or deep learning models on these devices can be challenging due to the high processing power required. Further, AI applications often require substantial memory and storage capacity. By their very nature, edge devices lack sufficient RAM or storage to handle large models and datasets. Finally, edge devices tend to run on minimal power, often relying only on batteries. This raises a challenge as AI computations tend to be among the most power-intensive in computing.

Software and Algorithmic Challenges

Running AI models on edge devices, especially in light of the hardware challenges, requires the software and algorithms to be fully optimized. There are various techniques to optimize AI models, but these often reduce the overall accuracy and performance of the models. This alone is a significant challenge and combined with the value of AI’s – real time processing, creates a major obstacle to implementation. Additionally, the edge device ecosystem makes the software and algorithmic challenges the most difficult to navigate due to the variety of edge devices with differing hardware and software configurations. Ensuring that AI models are compatible across device architectures can be complex and requires extensive testing.

Security and Privacy Challenges

For basic deployments, edge devices are used to collect and process data. Thus, the challenges to data security in edge AI applications are quite similar to those presented by standard edge data security. Working in favor of security and privacy, AI computations happen locally in edge devices, meaning that data does not need to be sent to external servers, eliminating a major threat vector. However, ensuring that the local processing adheres to privacy standards is still a challenge. Edge AI computing systems can be vulnerable to cyberattacks where, for example, inputs are designed to deceive the AI model. Ensuring robustness against such attacks is critical.

While edge AI cybersecurity is a major point of vulnerability, the challenges seem to be roughly in line with standard edge cybersecurity risks. This is not to say that they are inherently safe, but rather that the vulnerabilities are the same sorts of vulnerabilities that manufacturers and end users are already familiar with.

Deployment and Maintenance

As noted above, deploying software across numerous edge devices can be a herculean task. The number and variety of devices present significant challenges, which are magnified when deploying AI applications. Managing updates, patches, and new deployments at scale requires robust infrastructure processes which, again, are magnified in AI applications. The computing environment presents challenges. Edge devices often operate in environments where network connectivity is unstable or intermittent. Ensuring that AI models can function reliably in such conditions presents a challenge to developers. Solving deployment and maintenance challenges is possible but often quite expensive.

Innovating with AI at Sealevel

Sealevel is partnering with customers across industries to address the rapidly increasing reliance on AI. Namely, a leader in tactical and enterprise communications industry relies on Sealevel designed and manufactured carrier boards that incorporate interfaces for AI technology in server deployments for the U.S. military. Beyond defense applications for AI, new uses for AI in industrial and medical instrumentation settings are increasing exponentially. We’re witnessing firsthand the challenges associated with AI that face developers and manufacturers. Addressing these issues requires a multi-faceted approach, involving advancements in hardware technology, improvements in AI algorithms, robust security measures, and efficient deployment and maintenance strategies. The challenges are quite substantial, but with a careful, deliberate approach, they are possible to overcome.