VIVUM AI CASE STUDY
Evolutionary AI Empowers Autonomous Systems
Enhancing Efficiency and Adaptability
Introduction

In the realm of autonomous systems, efficiency and adaptability are key factors in determining success. This case study explores two projects that utilized Evolutionary AI, specifically Liquid Time-Constant Networks (LTCs), to enhance the autonomy of an airborne quadrotor drone and a land-based vehicle. The results demonstrate the superiority of Evolutionary AI over Conventional AI in terms of energy efficiency, resource utilization, and adaptability

Project 1

Quadrotor Drone

Flight Time Extension

CLIENT OBJECTIVE

Increase the flight time of a quadrotor drone

SOLUTION

Substituted CPU-based processing with the Vivum IP core on an FPGA board

Employed Evolutionary AI (LTC – CTRNN)

RESULTS

Extended flight time by 11.66%

Project 2

Land-Based Vehicle

Range Comparison

COMPUTE FABRICS COMPARED:

  • Nvidia Pegasus

  • Self-driving chip (S1) running on Conventional AI for sensor fusion

  • Vivum IP core

RESULTS
Vivum IP core vs. Nvidia Pegasus:

66.65% additional battery/miles (from 232 miles to 387 miles)

Vivum IP core vs. S1:

40.04% additional battery/miles (from 232 miles to 324 miles)

Advantages

Evolutionary AI in Autonomous Systems

EFFICIENCY
Achieves the same performance while consuming less energy
ADAPTABILITY
Adapts to changing situations and dynamic environments
OPTIMIZATION
Solves hard optimization problems, such as maximizing flight time and vehicle range
INNOVATION
Produces novel and efficient designs and strategies unlikely to be conceived by human experts
Conclusion

These case studies showcase the potential of Evolutionary AI in enhancing the autonomy of various systems. By demonstrating superior energy efficiency, resource utilization, and adaptability compared to conventional AI, Evolutionary AI proves its effectiveness in real-world applications. The projects successfully met the requirements set by the sponsors, highlighting the potential of Evolutionary AI in pushing the boundaries of autonomous systems.

As the demand for efficient and adaptable autonomous systems continues to grow, Evolutionary AI presents a promising approach to tackle the challenges faced by industries such as drone technology and autonomous vehicles. The ability to optimize performance while minimizing resource consumption positions Evolutionary AI as a key player in shaping the future of autonomous systems.

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