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Braille–Latin conversion using memristive bidirectional associative memory neural network
Artificial neural networks (ANNs) are finding increasing use as tools to model and solve problems in almost every discipline in today’s world. The...
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In-Memory Computing with Crosspoint Resistive Memory Arrays for Machine Learning
Memristor-based hardware accelerators play a crucial role in achieving energy-efficient big data processing and artificial intelligence, overcoming...
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In-Memory Computing for AI Accelerators: Challenges and Solutions
In-memory computing (IMC)-based hardware reduces latency as well as energy consumption for compute-intensive machine learning (ML) applications. Till...
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Combinational logic circuits based on a power- and area-efficient memristor with low variability
The saturation of complementary metal–oxide–semiconductor (CMOS) technology in terms of area and power efficiency has given rise to advanced research...
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FAMCroNA: Fault Analysis in Memristive Crossbars for Neuromorphic Applications
Resistive memories have drawn the attention of researchers due to their low power and single-cycle computation of vector-matrix multiplication (VMM),...
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Analog Computation with RRAM and Supporting Circuits
In-memory computing on RRAM crossbars enables efficient and parallel vector-matrix multiplication. The neural network weight matrix is mapped onto...
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Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials
The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing...
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Design and Control of an Autonomous Bat-like Perching UAV
Perching allows small Unmanned Aerial Vehicles (UAVs) to maintain their altitude while significantly extending their flight duration and reducing...
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Memristive discrete chaotic neural network and its application in associative memory
Chaotic behaviors existing in biological neurons play an important role in the brain’s associative memory. Hence, chaotic neural networks have been...
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Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence
Artificial intelligence applications have changed the landscape of computer design, driving a search for hardware architecture that can efficiently...
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The EGM Model and the Winner-Takes-All (WTA) Mechanism for a Memristor-Based Neural Network
Due to the continuous growth of hardware neuromorphic systems, the need for high-speed, low-power, and energy-efficient computer architectures is...
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Energy-efficient memcapacitor devices for neuromorphic computing
Data-intensive computing operations, such as training neural networks, are essential for applications in artificial intelligence but are energy...
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Synthese für Logic-in-Memory-Computing mit RRAM
In diesem Kapitel wird ein umfassender Ansatz für die Synthese von resistiven In-Memory-Computing-Schaltungen vorgestellt, der binäre...
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ReRAM-Based Neuromorphic Computing
Neuromorphic computing systems are faster and more energy efficient compared to von Neumann computing architectures because of their ability to...
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Neuromorphic Vision Based on van der Waals Heterostructure Materials
The human vision system represents the most intelligent camera capable of sensing and perceiving the world in a real-time manner. It has long been...
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Accelerating Deep Neural Networks with Phase-Change Memory Devices
In this chapter, we discuss recent advances in the hardware acceleration of deep neural networks with analog memory devices. Analog memory offers...
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In-memory computing: characteristics, spintronics, and neural network applications insights
In today's digital computing landscape, In-Memory Computing (IMC) has emerged as a revolutionary approach to tackling critical energy efficiency and...
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Formation of a Memristive Array of Crossbar-Structures Based on (Co40Fe40B20)x(LiNbO3)100 Nanocomposite
AbstractThe possibility of scaling of recently developed memristors of a new type based on (Co 40 Fe 40 B 20 ) x (LiNbO 3 ) 100 – x is shown. The scaling is...
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Kompilierung und Schreibausgleich für programmierbare Logik-in-Memory-Architekturen
In diesem Kapitel wird ein effizientes und vollautomatisches Kompilierungsverfahren vorgestellt, mit dem sich beliebige boolesche Funktionen in...
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Anomalous resistive switching in memristors based on two-dimensional palladium diselenide using heterophase grain boundaries
The implementation of memristive synapses in neuromorphic computing is hindered by the limited reproducibility and high energy consumption of the...
