Experimental Study of Artificial Neural Networks Using a Digital Memristor Simulator

Ntinas, Vasileios; Vourkas, Ioannis; Abusleme, Angel; Sirakoulis, Georgios Ch.; Rubio, Antonio

doi:10.1109/tnnls.2018.2791458

Cited by 54 publications

(29 citation statements)

References 37 publications

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“…In contrast, the memristor emulators are easy to obtain and cost efficient. The reported memristor emulators can be divided into two types: analog emulators are on basis of off‐the‐shelf electronic components 29–33 ; digital emulators mainly rely on a microcontroller 34,35 . In 2019, we designed an analog emulator for the threshold‐type binary memristor, where the threshold sensitive behavior is implemented by the diodes 32 .…”

Section: Introductionmentioning

confidence: 99%

Threshold‐type memristor‐based memory circuit

Liang

et al. 2021

Circuit Theory & Apps

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Summary Memory is of central importance to the research of high‐level cognitive behaviors of humans, where the plastic synapse contributes to the memory formation. Due to the controllable conductance and high‐density integration, the memristor acts as a promising candidate of artificial synapse. In terms of memory retention time, memory contains short‐term memory (STM) and long‐term memory (LTM). Additionally, the STM can be changed to the LTM via repeated learning, and this process is called memory consolidation. A threshold‐type memristor combined with basic operational circuits is used to design a memory circuit that can emulate various memory hierarchy levels containing the STM, the LTM, and the STM‐to‐LTM transition. Threshold firing behavior can also be emulated because of the threshold characteristic of the memristor. Furthermore, a floating emulator circuit that consists of several off‐the‐shelf devices is developed to mimic the electrical behaviors of the threshold‐type memristor, and its hardware circuit implementation is presented. Finally, both simulation and experimental results validate the feasibility and effectiveness of the proposed memory circuit.

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Section: Introductionmentioning

confidence: 99%

Threshold‐type memristor‐based memory circuit

Liang

et al. 2021

Circuit Theory & Apps

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“…Memristor possesses the significant advantage of resistance-tunable features for behaving like a synapse and is universally considered to be the emerging building blocks of brain-like ANNs (Wang et al, 2016;Li C. et al, 2018;Liu et al, 2018;Yan et al, 2018Yan et al, , 2019aYoon et al, 2018;Zhao et al, 2020). Some progress has been made in emulating synapse behavior and constructing hardware ANNs with memristors devices (Wang et al, 2012;Kim et al, 2015;Zhang et al, 2017;Hu et al, 2018;Ntinas et al, 2018). Prezioso et al (2015) experimentally demonstrated an artificial neural network using metal-oxide based memristors integrated into a dense, transistor-free crossbar circuit.…”

Section: Introductionmentioning

confidence: 99%

Self-Powered Memristive Systems for Storage and Neuromorphic Computing

et al. 2021

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A neuromorphic computing chip that can imitate the human brain’s ability to process multiple types of data simultaneously could fundamentally innovate and improve the von-neumann computer architecture, which has been criticized. Memristive devices are among the best hardware units for building neuromorphic intelligence systems due to the fact that they operate at an inherent low voltage, use multi-bit storage, and are cost-effective to manufacture. However, as a passive device, the memristor cell needs external energy to operate, resulting in high power consumption and complicated circuit structure. Recently, an emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, had the potential to perfectly solve the above problems. It has attracted great interest due to the advantages of its power-free operations. In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing. The review also proves a perspective on the application of artificial intelligence with the self-powered memristive system.

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“…However, the memristor device is still an immature technology and faces some challenges at the device and architectural levels which delays its availability as a commercial component. On the other hand, several emulator circuits have been reported in the literature to imitate the behavior of real memristor devices [19]. Field-Programmable Gate Array (FPGA) plat- [3] ∼ 80 [8] <2 [7] Switching speed (ns) 20 [3] 250 [8] 5 [7] FIGURE 1: Typical CNN architecture with the four main layers: Convolution, Pooling, ReLU and fully connected [9].…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based Memristor Emulator Circuit for Binary Convolutional Neural Networks

et al. 2020

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Binary convolutional neural networks (BCNN) have been proposed in the literature for resource-constrained IoTs nodes and mobile computing devices. Such computing platforms have strict constraints on the power budget, system performance, processing and memory capabilities. Nonetheless, the platforms are still required to efficiently perform classification and matching tasks needed in various applications. The memristor device has shown promising results when utilized for in-memory computing architectures, due to its ability to perform storage and computation using the same physical element. This is in addition to their nonlinear dynamic characteristics that can be used for various applications. Emulating the memristor operation through FPGAs implementation provides flexibility for rapid prototyping and exploration of the design space. In this paper, a FPGA-based memristor emulator circuit is implemented using a multi-bit XNOR gate as the main block for binary convolution which is followed by a memristorbased pooling layer. The BCNN layer is realized by bitwise XNOR-cell followed by wide NOR gate. The implementation has been successfully synthesized and verified using Xilinx Nexys4 FPGA with less than 1% utilization and 144.9 MHz frequency of operation. In order to test the functionality of the proposed cores, a digital implementation for the circuit was realized and verified experimentally. The experimental results show similar performance when compared to software simulation.

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Experimental Study of Artificial Neural Networks Using a Digital Memristor Simulator

Cited by 54 publications

References 37 publications

Threshold‐type memristor‐based memory circuit

Threshold‐type memristor‐based memory circuit

Self-Powered Memristive Systems for Storage and Neuromorphic Computing

FPGA-Based Memristor Emulator Circuit for Binary Convolutional Neural Networks

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