DIDACTIC: A Data-Intelligent Digital-to-Analog Converter with a Trainable Integrated Circuit using Memristors

Danial, Loai; Wainstein, Nicolás; Kraus, S.; Kvatinsky, Shahar

doi:10.1109/jetcas.2017.2780251

Cited by 19 publications

(12 citation statements)

References 31 publications

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“…The SAR-ADC realized a figure of merit (FoM) of 87.9 fJ/Conv.-step. The comparison between proposed 4 bit and 8 bit DAC and others memristor DAC [5,6,9] showed that highlighted low area and high resolution were achieved.…”

Section: Discussionmentioning

confidence: 95%

“…Moreover, it can be extended to high-resolution DAC without being limited by the memristor's properties. It is clear from Table 5 that the proposed DAC in [5] and [6] used a larger number of memristors and is limited in terms of extended resolutions, while the proposed DAC in [13] has the flexibility to increase DAC resolution. However, it required more transistors (at least one transistor for each extra bit (since the transistor area is larger than the memristor area)).…”

Section: Proposed Memristor Dac Comparisonmentioning

confidence: 99%

“…Therefore, there are a few different research directions for the use of memristors in DAC designs. For example, L. Danial et al were able to calibrate a DAC design configuration using neural networks as an artificial intelligence technique [6]. Another kind of research implemented hybrid DAC and ADC with a hybrid circuit using memristors.…”

Section: Introductionmentioning

confidence: 99%

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Design of a Memristor-Based Digital to Analog Converter (DAC)

Fahmy

Zorkany

2021

Electronics

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A memristor element has been highlighted in recent years and has been applied to several applications. In this work, a memristor-based digital to analog converter (DAC) was proposed due to the fact that a memristor has low area, low power, and a low threshold voltage. The proposed memristor DAC depends on the basic DAC cell, consisting of two memristors connected in opposite directions. This basic DAC cell was used to build and simulate both a 4 bit and an 8 bit DAC. Moreover, a sneak path issue was illustrated and its solution was provided. The proposed design reduced the area by 40%. The 8 bit memristor DAC has been designed and used in a successive approximation register analog to digital converter (SAR-ADC) instead of in a capacitor DAC (which would require a large area and consume more switching power). The SAR-ADC with a memristor-based DAC achieves a signal to noise and distortion ratio (SNDR) of 49.3 dB and a spurious free dynamic range (SFDR) of 61 dB with a power supply of 1.2 V and a consumption of 21 µW. The figure of merit (FoM) of the proposed SAR-ADC is 87.9 fj/Conv.-step. The proposed designs were simulated with optimized parameters using a voltage threshold adaptive memristor (VTEAM) model.

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Section: Discussionmentioning

confidence: 95%

Section: Proposed Memristor Dac Comparisonmentioning

confidence: 99%

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Design of a Memristor-Based Digital to Analog Converter (DAC)

Fahmy

Zorkany

2021

Electronics

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“…In this circuit, it is important to ensure that the transistor is in a linear state. The drawback of such circuit is a size of the synapse, which is appropriate for small-scale problems [67], and can be a critical issue for large-scale edge computing systems.…”

Section: A Inspiration From Biological Conceptsmentioning

confidence: 99%

Neuromemristive Circuits for Edge Computing: A Review

Krestinskaya

James

Chua

2020

IEEE Trans. Neural Netw. Learning Syst.

218

120

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The volume, veracity, variability and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing.

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“…Due to their high switching speed, low operating power, scalability, and high endurance [35], memristors are considered as attractive candidates to replace conventional memory and storage technologies (e.g., DRAM and Flash). Memristive technologies have also been explored for additional applications such as analog and radiofrequency circuits [36][37][38][39][40][41][42], neuromorphic circuits [43][44][45][46][47][48], and logic circuits, which are the focus of this paper. Different methods for using memristors to perform logical operations have been proposed.…”

Section: Memristive Logic Approachesmentioning

confidence: 99%

Real Processing-in-Memory with Memristive Memory Processing Unit (mMPU)

Kvatinsky¹

2019

2019 IEEE 30th International Conference on Application-Specific Systems, Architectures and Processors (ASAP)

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Memristive technologies are attractive candidates to replace conventional memory technologies and can also be used to perform logic and arithmetic operations. In this paper, we show how memristors are used to combine data storage and computation in the memory, thus enabling a novel non-von Neumann architecture called the 'memristive memory processing unit' (mMPU). The mMPU relies on a memristive logic technique called 'memristor aided logic' (MAGIC) that requires no modification to the memory array structure. By greatly reducing the data transfer between the CPU and the memory, the mMPU alleviates the primary restriction on performance and energy efficiency in modern computing systems. This paper describes basic principles and design considerations of MAGIC and the mMPU and presents a case study of digital image processing to demonstrate the benefits.

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DIDACTIC: A Data-Intelligent Digital-to-Analog Converter with a Trainable Integrated Circuit using Memristors

Cited by 19 publications

References 31 publications

Design of a Memristor-Based Digital to Analog Converter (DAC)

Design of a Memristor-Based Digital to Analog Converter (DAC)

Neuromemristive Circuits for Edge Computing: A Review

Real Processing-in-Memory with Memristive Memory Processing Unit (mMPU)

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