Is Generative AI about to revolutionize embedded systems? The arrival of large language models (LLMs) like ChatGPT and Gemini has demonstrated the incredible potential of generative AI, but these models are resource-intensive, demanding massive computing power. How can embedded systems, with their limited hardware, leverage this technology? The answer lies in adaptation and innovation.
## The Generative AI Advantage for Embedded Systems
Despite the challenges, the allure of generative AI for embedded systems is strong. Its ability to “remember” and contextualize new data opens doors to more intelligent and autonomous systems. This translates to better natural language understanding, text generation, implementation of complex commands, and smarter responses to sensor inputs. Imagine smart glasses providing real-time translation or medical devices that learn user behavior and make independent decisions. These possibilities are driving the need for generative AI at the edge.
## Overcoming Hardware Limitations
The memory footprint of LLMs, often measured in terabytes, poses a significant obstacle. Even with techniques like quantization, shrinking these models to fit within the constraints of embedded systems is nearly impossible. The solution involves bypassing LLMs altogether and embracing models specifically designed for limited resources. Small language models (SLMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) enhanced with generative AI elements are emerging as viable alternatives.
## The Future of Control Circuitry
These new AI models tailored for edge computing place unique demands on control circuits. Today’s AI-capable circuits excel at tasks like facial and keyword recognition, achieving hundreds of giga operations per second (GOPS). However, generative AI at the edge will require a leap to 10 tera operations per second (TOPS) by 2030. This necessitates advanced architectures that combine CPUs, GPUs, and dedicated neural processing units (NPUs), all while maintaining power efficiency and cost-effectiveness. The future of embedded systems lies in control circuits that can handle the computational demands of generative AI, bringing intelligent and autonomous capabilities to a wide range of applications.
