The data processing efficiency of traditional computers is suffering from the intrinsic limitation of physically separated processing and memory units. Logic-in-memory and brain-inspired neuromorphic computing are promising in-memory computing paradigms for improving the computing efficiency and avoiding high power consumption caused by extra data movement. However, memristors that can conduct digital memcomputing and neuromorphic computing simultaneously are limited by the difference in the information form between digital data and analogue data. In order to solve this problem, this paper proposes a flexible low-dimensional memristor based on boron nitride (BN), which has ultralow-power non-volatile memory characteristic, reliable digital memcomputing capabilities, and integrated ultrafast neuromorphic computing capabilities in a single in situ computing system. The logic-in-memory basis, including FALSE, material implication (IMP), and NAND, are implemented successfully. The power consumption of the proposed memristor per synaptic event (198 fJ) can be as low as biology (fJ level) and the response time (1 μs) of the neuromorphic computing is four orders of magnitude shorter than that of the human brain (10 ms), paving the way for wearable ultrahigh efficient next-generation in-memory computing architectures.
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http://dx.doi.org/10.1039/d0mh01730b | DOI Listing |
Adv Mater
January 2025
School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China.
The increasing demand for mobile artificial intelligence applications has elevated edge computing to a prominent research area. Silicon materials, renowned for their excellent electrical properties, are extensively utilized in traditional electronic devices. However, the development of silicon materials for flexible neuromorphic computing devices encounters great challenges.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, I-16145, Genoa, Italy.
Mixed signal analog/digital neuromorphic circuits represent an ideal medium for reproducing bio-physically realistic dynamics of biological neural systems in real-time. However, similar to their biological counterparts, these circuits have limited resolution and are affected by a high degree of variability. By developing a recurrent spiking neural network model of the retinocortical visual pathway, we show how such noisy and heterogeneous computing substrate can produce linear receptive fields tuned to visual stimuli with specific orientations and spatial frequencies.
View Article and Find Full Text PDFNat Commun
January 2025
School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
Biological neural circuits demonstrate exceptional adaptability to diverse tasks by dynamically adjusting neural connections to efficiently process information. However, current two-dimension materials-based neuromorphic hardware mainly focuses on specific devices to individually mimic artificial synapse or heterosynapse or soma and encoding the inner neural states to realize corresponding mock object function. Recent advancements suggest that integrating multiple two-dimension material devices to realize brain-like functions including the inter-mutual connecting assembly engineering has become a new research trend.
View Article and Find Full Text PDFNat Commun
January 2025
State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, China.
Complex-valued neural networks process both amplitude and phase information, in contrast to conventional artificial neural networks, achieving additive capabilities in recognizing phase-sensitive data inherent in wave-related phenomena. The ever-increasing data capacity and network scale place substantial demands on underlying computing hardware. In parallel with the successes and extensive efforts made in electronics, optical neuromorphic hardware is promising to achieve ultra-high computing performances due to its inherent analog architecture and wide bandwidth.
View Article and Find Full Text PDFNat Commun
January 2025
Key Laboratory of Brain like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Hebei University, Baoding, Hebei, China.
Neuromorphic computing holds immense promise for developing highly efficient computational approaches. Memristor-based artificial neurons, known for due to their straightforward structure, high energy efficiency, and superior scalability, which enable them to successfully mimic biological neurons with electrical devices. However, the reliability of memristors has always been a major obstacle in neuromorphic computing.
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