An Associative-Memory-Based Reconfigurable Memristive Neuromorphic System With Synchronous Weight Training

Yang, Le; Zeng, Zhigang; Huang, Y. L.

doi:10.1109/tcds.2019.2932179

Cited by 22 publications

(10 citation statements)

References 47 publications

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“…Considering the memristive neuromorphic circuits used for computation acceleration, the work [42], [43], [44], showed that the synaptic circuit is required to represent process variable weights in binary form. The works equally highlighted that various synaptic circuits can be designed to utilize neuromorphic circuits.…”

Section: B Neuromorphic State Machine (Nsm)mentioning

confidence: 99%

“…The works equally highlighted that various synaptic circuits can be designed to utilize neuromorphic circuits. At the implementation level, Field programmable gate arrays (FPGAs) have been used to provide neuromorphic reconfigurable characteristics while enabling flexible schematic designs for various applications [44], [45], [46], [47]. Interestingly, various control circuit topologies designated for process variables in existing designs lack the functionality of MRASM states.…”

Section: B Neuromorphic State Machine (Nsm)mentioning

confidence: 99%

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ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

Okafor¹,

Longe²

2022

IJACSA

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In the next 5 to 10 years, digital Artificial Intelligence with Machine Circuit Learning Algorithms (MCLA) will become the mainstream in complex automated robots. Its power concerns, ethical perspectives, including the issues of digital sensing, actuation, mobility, efficient processcomputation, and wireless communication will require advanced neuromorphic process variable controls. Existing home automated robots lack memristic associative memory. This work presents Cyber-Physical Home Automation System (CPHAS) using Memristive Reconfigurable Algorithmic State Machine (MRASM) chart. A process control architecture that supports Concurrent Wireless Data Streams and Power-Transfer (CWDSPT) is developed. Unlike legacy systems with powersplitting (PS) and time-switching (TS) controls, the MRASM-ROBOT explores granular wireless signal controls through unmodulated high-power continuous wave (CW). This transmits infinite process variables using Orthogonal Space-Time Block Code (OSTBC) for interference reduction. The CWDSPT transmitter and receiver circuit design for signal processing are implemented with complexity noise-error reduction during telemetry data decoding. Received signals are error-buffered while gathering control variables' status.Transceiver Memristive neuromorphic circuits are introduced for computational acceleration in the design. Hardware circuit design is tested for system reliability considering the derived schematic models for all process variables. Under small range space diversity, the system demonstrated significant memory stabilization at the synchronous iteration of the synaptic circuitry.

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Section: B Neuromorphic State Machine (Nsm)mentioning

confidence: 99%

Section: B Neuromorphic State Machine (Nsm)mentioning

confidence: 99%

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

Okafor¹,

Longe²

2022

IJACSA

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“…Because of the fascinating capabilities of the human brain, such as parallel processing, error resilience (working with approximate data), and the most remarkable feature, learning capacity, bio-inspired computational systems have become highly successful [1][2][3]. Bio-inspired computational systems show great potential to overcome Von-Neumann-based computers' inefficiency in processing complex tasks, such as image processing and pattern association [4][5][6][7]. Considering the features mentioned above of the bio-inspired computational systems, hardware implementation of these systems has also become one of today's hottest research topics [6,8].…”

Section: Introductionmentioning

confidence: 99%

A high‐capacity and nonvolatile spintronic associative memory hardware accelerator

Rezaei

Amirany

Moaiyeri

et al. 2023

IET Circuits, Devices & Syst

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Significant progress has been made in manufacturing emerging technologies in recent years. This progress implemented in-memory-computing and neural networks, one of today's hottest research topics. Over time, the need to process complex tasks has increased. This need causes the emergence of intelligent processors. A nonvolatile associative memory based on spintronic synapses utilising magnetic tunnel junction (MTJ) and carbon nanotube field-effect transistors (CNTFET)-based neurons is proposed. The proposed design uses the MTJ device because of its fascinating features, such as reliable reconfiguration and nonvolatility. At the same time, CNTFET has overcome conventional complementary metal-oxide-semiconductor shortcomings like the short channel effect, drain-induced barrier lowering, and poor hole mobility. The proposed design is simulated in the presence of process variations. The proposed design aims to increase the number of weights generated in the synapse for higher memory capacity and accuracy. The effect of different tunnel magnetoresistance (TMR) values (100%, 200%, and 300%) on the performance and accuracy of the proposed design has also been investigated. This investigation shows that the proposed design performs well even with a low TMR value, which is very important and remarkable from the fabrication point of view.

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“…[12][13][14] The theoretical memristor was put forward by Chua. [18][19][20] Until HP labs manufactured the first physical memristor, 21 the articles on memristor have been growing more and more extensively. [18][19][20] Until HP labs manufactured the first physical memristor, 21 the articles on memristor have been growing more and more extensively.…”

Section: Introductionmentioning

confidence: 99%

“…15 Memristor not only has nonvolatility to keep memristance unchanged 16,17 but also has adjustable property that the voltage on memristor can change the memristance as well. [18][19][20] Until HP labs manufactured the first physical memristor, 21 the articles on memristor have been growing more and more extensively. [22][23][24][25][26] Hopfield neural network can convert analog input signal to digital signal by accurately arranging every resistance in the network.…”

Section: Introductionmentioning

confidence: 99%

A memristive dual‐slope A/D converter

Yang

Zeng

et al. 2019

Circuit Theory & Apps

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As an emerging device, memristor has several excellent properties like changeable memristance, nonvolatility, and nanoscale. Based on complementary metal-oxide-semiconductor (CMOS) dual-slope analog-to-digital (A/D) converter, this paper proposes a memristive dual-slope A/D converter. Owing to the usage of memristor, the proposed memristive A/D converter not only has more compact circuit structure and simpler control timing than the CMOS one but also has advantages over the existing memristive conversion circuits in circuit design and application. For the memristive A/D converter, a conversion process consists of two count procedures. By means of controlling the memristance change in the two count procedures, the A/D converter converts an analog signal to the corresponding digital count value. Meanwhile, the conversion result is inferred according to the circuit structure of the A/D converter. Then, combining the conversion process and PSPICE simulation, this paper analyzes the anti-interference performance of the A/D converter. Further, the robustness of the A/D converter is presented, applying the similar analysis methods. The analysis results demonstrate that the proposed A/D converter has good anti-interference and robustness performances.

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An Associative-Memory-Based Reconfigurable Memristive Neuromorphic System With Synchronous Weight Training

Cited by 22 publications

References 47 publications

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

A high‐capacity and nonvolatile spintronic associative memory hardware accelerator

A memristive dual‐slope A/D converter

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