With ChatGPT driving unprecedented interest in generative AI, the technology landscape is evolving rapidly. On-device AI, in particular, has emerged as a frontrunner in AI industry trends. Today, we'll explore the real challenges companies face when implementing on-device AI solutions.

If you're reading this, you're likely an engineer interested in on-device AI or a business leader considering AI implementation. While we'll cover the growing prominence of on-device AI in detail in another post, it's worth noting that many AI companies are exploring this technology primarily for cost efficiency and service reliability.

Despite growing interest and demand for on-device AI technology, implementation remains challenging. Here are some common concerns we hear from the field:

"We want to implement on-device AI, but we're worried about performance degradation."

"Finding on-device AI experts is extremely challenging."

"We've already invested significantly in AI service development, but on-device AI implementation isn't progressing as expected."

"We're developing on-device AI internally, but should we consider partnering with specialized companies?"

If you've had any of these concerns, congratulations! You're not alone. These challenges can be distilled into three key issues:

First: Performance Concerns

While on-device AI might seem like a perfect solution at first glance, the reality is more complex. Compressing AI models that originally run on powerful cloud servers to operate on device chips carries significant risks, often resulting in increased error rates. No company wants to implement a lower-performing model, regardless of the importance of on-device AI.

However, there's a solution. Neural Processing Units (NPUs), specialized for AI computations, can effectively run AI on devices. Surprisingly, NPUs have been built into most smartphones released since 2018, with continuously improving performance and energy efficiency.

Second: Lack of Expertise

On-device AI is a nascent technology with few experts in the field. It requires profound knowledge of both AI and processors, making it challenging to find specialists who can handle everything from AI model development to hardware optimization. Moreover, since different devices operate on different processor architectures, building a team of experts becomes even more challenging.

Third: Resource Constraints

Developing AI models and implementing on-device AI are entirely different challenges. Typically, on-device AI implementation requires a dedicated team and at least six months of development time. For companies that have already invested heavily in AI technology and are close to commercialization, investing additional time in on-device AI implementation can seem like a burden from a short-term perspective.

One Solution: ZETIC.ai

ZETIC.ai offers a comprehensive solution addressing all three challenges:

• Minimal performance loss through NPU optimization technology

• Expert team specialized in on-device AI

• Support for 99% of mobile environments worldwide

• On-device AI implementation within 24 hours

• Ready-to-use AI module library (Try Demo)

Conclusion

The AI field is resource-intensive, talent-scarce, and often involves significant trial and error. Nevertheless, the AI industry is experiencing unprecedented growth. For successful AI service implementation, finding the most efficient and effective method is crucial. Let ZETIC.ai be your reliable partner in this journey.

Ready to work with world-class on-device AI experts? Have questions? Contact us anytime at contact@zetic.ai.