Chiplets and Heterogeneous Computing for Robotics Design

Chiplets and heterogeneous computing meet the complex design challenges of robotics development.

By Giordana Francesca Brescia, embedded.com

Robotics places high demands on power, efficiency, and flexibility. The new architectures of chiplets and heterogeneous computing are an answer to the limitations of traditional system-on-chips (SoCs). It is a technological evolution designed to fundamentally change intelligent robotic systems and offer new possibilities for designers in terms of performance and complexity.

As embedded applications become increasingly complex, robotic systems have made a quantum leap from rigidly programmed ecosystems to intelligent platforms that can sense, learn, and adapt to their surroundings. Advanced industrial robots, autonomous vehicles, drones, and collaborative systems all share a common characteristic: the need to process massive amounts of data in real time.

High-resolution computer vision, sensor fusion, dynamic planning, and AI models require increasingly high computational resources. Traditionally, these needs have been met through monolithic SoCs, in which the CPU, GPU, memory, and peripherals are integrated on a single die.

However, this model has encountered more limitations over time. For example, increasing the die size reduces production yield, increases costs, and introduces thermal complexities that are difficult to manage. In addition, the need to use different technology nodes for different functions makes a completely monolithic integration inefficient.

Chiplets introduce modularity in semiconductor design, while heterogeneous computing leverages specialized processing units for specific tasks. The combination of these two technologies meets the complex design challenges of advanced robotics.

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