Autonomously Operated Synapses Instantly Humanize Artificial Intelligence Equipment's Vision Capabilities
🤖 Bold new technology, bright future:
In a groundbreaking innovation, researchers at Tokyo University of Science have constructed a self-powered artificial synapse that boasts near-human precision in color recognition. This device mirrors the biological vision system and generates its own power using solar cells, offering a solution to traditional systems' energy-intensive requirements.
The device proudly displays a resolution of 10-nanometers in distinguishing colors across the visible spectrum, enabling logic functions based on light wavelengths. With this development, machine vision in edge devices like smartphones, smartwatches, and autonomous vehicles can tackle smaller, more efficient visual processing tasks without draining battery life.
As modern AI and smart devices continue their evolution, machine vision assumes a critical role in driving state-of-the-art technologies. While advancements have been made, these systems face significant challenges in managing vast amounts of visual data, requiring extensive power, storage, and computational resources.
This limitation restricts the deployment of visual recognition capabilities in edge devices such as smartphones, drones, and autonomous vehicles. Fortunately, the human visual system provides a blueprint for an efficient alternative model. Unlike conventional machine vision systems, our eyes and brains selectively address information, leading to higher processing efficiency and minimal power consumption.
Neuromorphic computing, which mimics the structure and function of biological neural systems, represents a promising route to overcome existing hurdles in computer vision. However, challenges remain. Firstly, achieving color recognition comparable to human vision has been difficult, and secondly, complex systems have required external power sources to conserve energy.
To combat these issues, Takashi Ikuno and his team from the Tokyo University of Science have devised an innovative solution. Introduced in a study published in Scientific Reports, their self-powered artificial synapse can distinguish colors with impressive precision.
The researchers crafted their device by pairing two different dye-sensitized solar cells that react differently to varying wavelengths of light. Unlike conventional optoelectronic artificial synapses, this device generates electricity through energy conversion. This self-powering ability makes it particularly fitting for edge computing applications, where energy efficiency is of paramount importance.
In extensive experiments, the team demonstrated the system's remarkable color discrimination capabilities, with a resolution similar to the human eye across the visible spectrum. Furthermore, the device produced bipolar responses, generating positive voltage under blue light and negative voltage under red light. This bipolar behavior allows intricate logical operations which typically require multiple devices.
"The results hold great promise for the application of this state-of-the-art optoelectronic device, capable of simultaneous high-resolution color discrimination and logical operations, in low-power AI systems with visual recognition," remarks Dr. Ikuno.
As a proof-of-concept, the team implemented their device within a physical reservoir computing framework to recognize different human movements recorded in red, green, and blue. The system achieved an impressive 82% accuracy when classifying 18 different combinations of colors and movements using just one device.
The impact of this research transcends various industries. Inself-driving cars, for instance, these devices could enhance the recognition of traffic lights, road signs, and pedestrians. Within healthcare, they could power wearable devices that monitor vital signs like blood oxygen levels with minimal battery drain. In consumer electronics, this technology could lead to smartphones and augmented/virtual reality headsets with prolonged battery life and sophisticated visual recognition capabilities.
"We anticipate that this technology will contribute to the emergence of low-power machine vision systems with color discrimination abilities on par with the human eye, with potential applications ranging from optical sensors for self-driving cars to low-power biometric sensors for medical use and portable recognition devices," remarks Dr. Ikuno.
In sum, this work marks a step in the right direction as we seek to bring the marvels of computer vision to edge devices, bridging the gap between digital and human perception.
Funding: This work was supported by the JST and the establishment of university fellowships for the creation of science and technology innovation (Grant Number JPMJFS2144). Additional support was provided by the JST SPRING (Grant Number JPMJSP2151).
More Information:
About this visual neuroscience and AI research news
Author: Yoshimasa IwasakiSource: Tokyo University of ScienceContact: Yoshimasa Iwasaki - Tokyo University of ScienceImage: Credit to our website
Original Research: Open access. "Polarity-tunable dye-sensitized optoelectronic artificial synapses for physical reservoir computing-based machine vision" by Takashi Ikuno et al., Scientific Reports.
- Advancements in artificial intelligence and technology are escalating, with the human visual system serving as a blueprint for efficient machine vision.
- Neuromorphic computing, which replicates the structure and function of biological neural systems, offers a promising route to address the limitations faced by conventional machine vision systems.
- A breakthrough in this field comes from the Tokyo University of Science, as researchers have constructed a self-powered artificial synapse that achieves near-human precision in color recognition, a battery-efficient solution for machine vision in edge devices.
- The latest neuroscience news unfolds on this research, published in Scientific Reports, where the device, powered by dye-sensitized solar cells, generates electricity through energy conversion and exhibits impressive color discrimination capabilities.
