Physical Review B

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

Modern computers are based on the von Neumann architecture in which computation and storage are physically separated: data are fetched from the memory unit, shuttled to the processing unit (where computation takes place) and then shuttled back to the memory unit to be stored. The rate at which data can be transferred between the processing unit and the memory unit represents a fundamental limitation of modern computers, known as the memory wall. In-memory computing is an approach that attempts to address this issue by designing systems that compute within the memory, thus eliminating the energy-intensive and time-consuming data movement that plagues current designs. Here we review the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, their resistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation. We examine the different digital, analogue, and stochastic computing schemes that have been proposed, and explore the microscopic physical mechanisms involved. Finally, we discuss the challenges in-memory computing faces, including the required scaling characteristics, in delivering next-generation computing. This Review Article examines the development of in-memory computing using resistive switching devices.

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In-memory computing with resistive switching devices

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Design Of Analog Cmos Integrated Circuits

Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory computing. In this Review, we provide a broad overview of the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing. This Review provides an overview of memory devices and the key computational primitives for in-memory computing, and examines the possibilities of applying this computing approach to a wide range of applications.

Memory devices and applications for in-memory computing

Defect tolerance will be critical in any system with nanoscale feature sizes. This article examines some fundamental aspects of defect tolerance for a reconfigurable system based on Quantum-dot Cellular Automata (QCA). We analyze a novel, QCA-based, Programmable Logic Array (PLA) structure, develop an implementation independent fault model, and discuss how expected defects and faults might affect yield. Within this context, we introduce techniques for mapping Boolean logic functions to a defective QCA-based PLA. Simulation results show that our new mapping techniques can achieve higher yields than existing techniques.

Defects and faults in QCA-based PLAs

Various implementations of the quantum-dot cellular automata (QCA) device architecture may help many performance scaling trends continue as we approach the nano-scale. Experimental success has led to the evolution of a research track that looks at QCA-based design. The work presented in this paper follows that track and looks at implementation friendly, programmable QCA circuits. Specifically, we analyze a novel, QCA-based, programmable logic array (PLA) structure, develop an implementation independent fault model, discuss how expected defects and faults might affect yield, and look at the design in the context of a magnetic implementation of QCA.

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Fault Models and Yield Analysis for QCA-Based PLAs

Emerging memory devices are an attractive choice for implementing very energy-efficient in-situ matrix-vector multiplication (MVM) for use in intelligent edge platforms. Despite their great potential, device-level non-idealities have a large impact on the application-level accuracy of deep neural network (DNN) inference. We introduce a low-density parity-check code (LDPC) based approach to correct non-ideality induced errors encountered during in-situ MVM. We first encode the weights using error correcting codes (ECC), perform MVM on the encoded weights, and then decode the result after in-situ MVM. We show that partial encoding of weights can maintain DNN inference accuracy while minimizing the overhead of LDPC decoding. Within two iterations, our ECC method recovers 60% of the accuracy in MVM computations when 5% of underlying computations are error-prone. Compared to an alternative ECC method which uses arithmetic codes, using LDPC improves AlexNet classification accuracy by 0.8% at iso-energy. Similarly, at iso-energy, we demonstrate an improvement in CIFAR-10 classification accuracy of 54% with VGG-11 when compared to a strategy that uses 2× redundancy in weights. Further design space explorations demonstrate that we can leverage the resilience endowed by ECC to improve energy efficiency (by reducing operating voltage). A 3.3× energy efficiency improvement in DNN inference on CIFAR-10 dataset with VGG-11 is achieved at iso-accuracy.

Embedding error correction into crossbars for reliable matrix vector multiplication using emerging devices

In order to continue the performance and scaling trends that we have come to expect from Moore’s Law, many emergent computational models, devices, and technologies are actively being studied to either replace or augment CMOS technology. Nanomagnet Logic (NML) is one such alternative. NML operates at room temperature, it has the potential for low power consumption, and it is CMOS compatible. In this aricle, we present an NML programmable logic array (PLA) based on a previously proposed reprogrammable quantum-dot cellular automata PLA design. We also discuss the fabrication and simulation validation of the circuit structures unique to the NML PLA, present area, energy, and delay estimates for the NML PLA, compare the area of NML PLAs to other reprogrammable nanotechnologies, and analyze how architectural-level redundancy will affect performance and defect tolerance in NML PLAs. We will use results from this study to shape a concluding discussion about, which architectures appear to be most suitable for NML.

A Reconfigurable PLA Architecture for Nanomagnet Logic

This paper details nano-scale devices being researched by physical scientists to build computational systems. It also reviews some existing system design work that uses the devices to be discussed. It concludes with a discussion of how the authors believe system-level research can best be used to positively affect actual device development. This work has led to a more thorough design methodology that address whether or not computationally interesting and buildable circuits are possible with the quantum-dot cellular automata (QCA), while also providing significant wins over end-of-the-roadmap CMOS.

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Michael Niemier

Papers

Defects and faults in QCA-based PLAs

Fault Models and Yield Analysis for QCA-Based PLAs

Embedding error correction into crossbars for reliable matrix vector multiplication using emerging devices

A Reconfigurable PLA Architecture for Nanomagnet Logic

Using circuits and systems-level research to drive nanotechnology