A Seamless, Reconfigurable, and Highly Parallel In-Memory Stochastic Computing Approach With Resistive Random Access Memory Array

Shen, Wensheng; Huang, Peng; Fan, Mengqi; Zhao, Yudi; Feng, Yansong; Liu, Lifeng; Liu, Xiaoyan; Zhang, Xing; Kang, Jinfeng

doi:10.1109/ted.2020.3037279

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…Note that the distribution of transistors and memristors in the logic gates differs from previous studies 31 , 32 . The circuit comparison can be found in Supplementary Fig.…”

Section: Resultscontrasting

confidence: 60%

Cellular automata imbedded memristor-based recirculated logic in-memory computing

Liu

Tian

et al. 2023

Nat Commun

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Memristor-based circuits offer low hardware costs and in-memory computing, but full-memristive circuit integration for different algorithm remains limited. Cellular automata (CA) has been noticed for its well-known parallel, bio-inspired, computational characteristics. Running CA on conventional chips suffers from low parallelism and high hardware costs. Establishing dedicated hardware for CA remains elusive. We propose a recirculated logic operation scheme (RLOS) using memristive hardware and 2D transistors for CA evolution, significantly reducing hardware complexity. RLOS’s versatility supports multiple CA algorithms on a single circuit, including elementary CA rules and more complex majority classification and edge detection algorithms. Results demonstrate up to a 79-fold reduction in hardware costs compared to FPGA-based approaches. RLOS-based reservoir computing is proposed for edge computing development, boasting the lowest hardware cost (6 components/per cell) among existing implementations. This work advances efficient, low-cost CA hardware and encourages edge computing hardware exploration.

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“…Note that the distribution of transistors and memristors in the logic gates differs from previous studies 31 , 32 . The circuit comparison can be found in Supplementary Fig.…”

Section: Resultscontrasting

confidence: 60%

Cellular automata imbedded memristor-based recirculated logic in-memory computing

Liu

Tian

et al. 2023

Nat Commun

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“…Over the last decade, the unpredictable behavior of RSMs has been demonstrated to be an efficient way to create true random number generators (Shen et al, 2021;Gaba et al, 2013;Yang et al, 2020b;Hu et al, 2016;Faria et al, 2018;Bao et al, 2020) that can be used for security purposes (Khan et al, 2021;Pang et al, 2019;Lv et al, 2020). For machine learning applications, the Gaussian nature of the stochastic distribution turns out to be an efficient way to implement probabilistic computing in hardware (Table 1).…”

Section: Rsm-based Methodsmentioning

confidence: 99%

Exploiting Non-idealities of Resistive Switching Memories for Efficient Machine Learning

et al. 2022

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Novel computing architectures based on resistive switching memories (also known as memristors or RRAMs) have been shown to be promising approaches for tackling the energy inefficiency of deep learning and spiking neural networks. However, resistive switch technology is immature and suffers from numerous imperfections, which are often considered limitations on implementations of artificial neural networks. Nevertheless, a reasonable amount of variability can be harnessed to implement efficient probabilistic or approximate computing. This approach turns out to improve robustness, decrease overfitting and reduce energy consumption for specific applications, such as Bayesian and spiking neural networks. Thus, certain non-idealities could become opportunities if we adapt machine learning methods to the intrinsic characteristics of resistive switching memories. In this short review, we introduce some key considerations for circuit design and the most common non-idealities. We illustrate the possible benefits of stochasticity and compression with examples of well-established software methods. We then present an overview of recent neural network implementations that exploit the imperfections of resistive switching memory, and discuss the potential and limitations of these approaches.

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“…The concept of in-memory computing was first proposed in 1960s [6], but did not receive enough attention. With the advent of memristor technology, the in-memory computing attracts much attention again [7], [8], [9]. And then, many emerging memory concepts are studied to construct the in-memory computing architecture [10], [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Highly Efficient Reconfigurable Stateful Logic Operations Based on CuI Memristor-Only Arrays Prepared With a Solution-Based Process

Luo

Wei

et al. 2023

IEEE J. Electron Devices Soc.

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The in-memory logic computing has been intensively studied as being considered as an important scenario to address the power-consumption issue posed by modern computers based on the von Neumann architecture. However, the realization of in-memory logic computing is generally based on memristors prepared by vacuum techniques and the implementation of logic operation requires multiple cycles of voltage pulses, which limits the computing efficiency. In addition, the logic architectures that cannot be mapped into large crossbar arrays restrict the level of parallelism. A CuI memristor is prepared by a solution-based process in this work. Based on the CuI memristor arrays, with delicately designed implement strategy, a set of logic operations can be implemented with one single cycle of voltage pulses, demonstrating the high efficiency. Moreover, all proposed logic architectures are able to be mapped to large memristor crossbar arrays directly, demonstrating the great potentials in computations with high degree of parallelism. Owing to the reconfigurable property, a one-bit full adder is realized by cascading the memristor-based logic gates. CuI, high parallelism, in-memory computing, logic gate, memristor. INDEX TERMS

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A Seamless, Reconfigurable, and Highly Parallel In-Memory Stochastic Computing Approach With Resistive Random Access Memory Array

Cited by 11 publications

References 19 publications

Cellular automata imbedded memristor-based recirculated logic in-memory computing

Cellular automata imbedded memristor-based recirculated logic in-memory computing

Exploiting Non-idealities of Resistive Switching Memories for Efficient Machine Learning

Highly Efficient Reconfigurable Stateful Logic Operations Based on CuI Memristor-Only Arrays Prepared With a Solution-Based Process

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