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Traditional robotic vehicle control algorithms, implemented on digital devices with firmware, result in high power consumption and system complexity. Advanced control systems based on different device physics are essential for the advancement of sophisticated robotic vehicles and miniature mobile robots. Here, we present a nanoelectronics-enabled analog control system mimicking conventional controllers’ dynamic responses for real-time robotic controls, substantially reducing training cost, power consumption, and footprint. This system uses a reservoir computing network with interconnected memristive channels made from layered semiconductors. The network’s nonlinear switching and short-term memory characteristics effectively map input sensory signals to high-dimensional data spaces, enabling the generation of motor control signals with a simply trained readout layer. This approach minimizes software and analog-to-digital conversions, enhancing energy and resource efficiency. We demonstrate this system with two control tasks: rover target tracking and drone lever balancing, achieving similar performance to traditional controllers with ~10-microwatt power consumption. This work paves the way for ultralow-power edge computing in miniature robotic systems.
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Programming memristor arrays with arbitrarily high precision for analog computing
In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches.
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- PAR ID:
- 10522054
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- AAAS
- Date Published:
- Journal Name:
- Science
- Volume:
- 383
- Issue:
- 6685
- ISSN:
- 0036-8075
- Page Range / eLocation ID:
- 903 to 910
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/science.adi9405
