The Rise of Edge AI: Powering Taiwan’s Next-Generation On-Device AI Applications

With the rapid advancement of generative AI technologies, edge computing is emerging as the next major battlefield. The shift from cloud-based to edge-based AI processing not only introduces new challenges but also creates unprecedented development opportunities for Taiwan’s tech industry—especially in areas such as IoT, smart homes, automotive systems, robotics, and industrial automation. Deploying AI models on the edge has become a critical factor in driving industrial transformation.

Surging Model Size Presents New Deployment Challenges

In contrast to early computer vision models with roughly 100 million parameters, today’s generative AI models often exceed 7 billion or even tens of billions of parameters, making edge deployment exponentially more difficult. The growing model size leads to two core issues: computational bottlenecks when handling encoder models and insufficient memory bandwidth for decoder models.

The rise of multi-modal models—which process voice, images, and text simultaneously—further exacerbates the challenge, as their encoder-decoder architectures demand greater hardware performance and energy efficiency, pushing the limits of traditional system architectures.

Cloud-based AI queries today typically consume hundreds of watts per inference. Reducing the power consumption of Edge AI devices has thus become a key driver of industry development. This trend has fueled the growing importance of model miniaturization and energy-saving technologies.

From Software-Hardware Integration to Energy Optimization: Taiwan’s Systematic Breakthrough

One major hurdle in edge AI development lies in the integration gap between software and hardware. Edge Force, a Taiwan-based innovator, addresses this with proprietary model miniaturization technology that designs ultra-efficient custom circuits to overcome computational bottlenecks. Their algorithms and systems are restructured into low-bit, high-efficiency formats, compressing cloud-scale AI models by 90% for edge deployment—while maintaining over 99% accuracy. With only 2–4-bit precision, real-time inference can be achieved even in offline environments, enabling safe, efficient, and immediate AI operation while significantly reducing energy consumption.

Edge Force’s strength in both software and hardware integration extends beyond custom AI IP design services. The company also offers a complete toolchain—from model training, conversion, quantization, compression, to deployment—forming a modular development pipeline that dramatically lowers the barrier for enterprises to adopt edge AI at every stage.

Vertical Applications Drive Real-World Opportunities

Since the second half of 2024, multi-modal models have risen quickly as key technologies to address complex use cases. Compared to traditional multi-model pipelines (e.g., speech-to-text → NLP → text-to-speech), unified multi-modal models can enable end-to-end processing, greatly simplifying system architecture and enhancing performance.

There is currently strong market demand for Small Language Models (SLLMs), particularly in smart manufacturing and home appliance sectors. These compact models already support voice recognition, image classification, and real-time responses—significantly enhancing product value.

According to Edge Force CEO Ethan Wu, “Edge AI will gradually redefine human-machine interfaces, reshaping how we interact with machines.” Edge Force’s newly launched low-latency speech-to-speech AI IP is enabling this shift—from traditional button-based interfaces to natural voice conversations. From operating washing machines and microwaves with speech to enabling industrial equipment Q&A, such solutions also offer a more inclusive environment for users with mobility challenges.

ASIC and Chip Design: A New Stage for Taiwan’s Hardware Strength

The value of Edge AI lies not only in the technology itself, but also in accurate market positioning. As Ethan Wu puts it, “Successful Edge AI products focus on targeted use cases, optimizing functionality and cost rather than offering overly broad, general-purpose solutions.”

Although developing custom AI chips (ASICs) is resource-intensive and poses significant go-to-market challenges, there is a growing need for high-efficiency, customized ASICs in the Edge AI sector. The key to success lies in clearly identifying the target market, deeply understanding the specific use-case requirements, and cultivating technical differentiation through focused R&D.

Unlike the cloud sector, which is dominated by a few major players, the edge hardware market is fragmented and lacks clear leaders—creating fertile ground for Taiwan’s small and mid-sized chip design firms to emerge. With strong product innovation, these companies have the opportunity to build competitive advantages in niche areas.

Taiwan’s Strategic Position and Potential

As a global semiconductor powerhouse, Taiwan boasts a complete IC design, foundry, and system integration ecosystem. Combined with its corporate agility in AI applications, Taiwan is uniquely positioned as an ideal testbed for Edge AI innovation and deployment.

Amid the global wave of digital transformation, Edge AI is not just a technological upgrade—it is also a catalyst for new business models. If Taiwan can continue to enhance its AI R&D capabilities, bridge cross-disciplinary talent gaps, and build a robust software-hardware integration supply chain, it will secure a critical role in the next generation of global AI competition.