Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks—at the level of small network subgraphs—remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.

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Higher-order organization of complex networks

A striking difference between brain-inspired neuromorphic processors and current von Neumann processor architectures is the way in which memory and processing is organized. As information and communication technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper, we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multineuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.

Memory and Information Processing in Neuromorphic Systems

Ultra-low voltage operation of memory cells has become a topic of much interest due to its applications in very low energy computing and communications. However, due to parameter variations in scaled technologies, stable operation of SRAMs is critical for the success of low-voltage SRAMs. It has been shown that conventional 6T SRAMs fail to achieve reliable subthreshold operation. Hence, researchers have considered different configuration SRAMs for subthreshold operations having single-ended 8T or 10T bit-cells for improved stability. While these bit-cells improve SRAM stability in subthreshold region significantly, the single-ended sensing methods suffer from reduced bit-line swing due to bit-line leakage noise. In addition, efficient bit-interleaving in column may not be possible and hence, the multiple-bit soft errors can be a real issue. In this paper, we propose a differential 10T bit-cell that effectively separates read and write operations, thereby achieving high cell stability. The proposed bit-cell also provides efficient bit-interleaving structure to achieve soft-error tolerance with conventional Error Correcting Codes (ECC). For read access, we employ dynamic DCVSL scheme to compensate bitline leakage noise, thereby improving bitline swing. To verify the proposed techniques, a 32 kb array of the proposed 10T bit-cell is fabricated in 90 nm CMOS technology. The hardware measurement results demonstrate that this bit-cell array successfully operates down to 160 mV. For leakage power comparison, we also fabricated 49 kb arrays of the 6T and the proposed 10T bit-cells. Measurement results show that the leakage power of the proposed bit-cell is close to that of the 6T (between 0.96x and 1.22x of 6T).

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A 32 kb 10T Sub-Threshold SRAM Array With Bit-Interleaving and Differential Read Scheme in 90 nm CMOS

The rapid development of information technology has led to urgent requirements for high efficiency and ultralow power consumption. In the past few decades, neuromorphic computing has drawn extensive attention due to its promising capability in processing massive data with extremely low power consumption. Here, we offer a comprehensive review on emerging artificial neuromorphic devices and their applications. In light of the inner physical processes, we classify the devices into nine major categories and discuss their respective strengths and weaknesses. We will show that anion/cation migration-based memristive devices, phase change, and spintronic synapses have been quite mature and possess excellent stability as a memory device, yet they still suffer from challenges in weight updating linearity and symmetry. Meanwhile, the recently developed electrolyte-gated synaptic transistors have demonstrated outstanding energy efficiency, linearity, and symmetry, but their stability and scalability still need to be optimized. Other emerging synaptic structures, such as ferroelectric, metal–insulator transition based, photonic, and purely electronic devices also have limitations in some aspects, therefore leading to the need for further developing high-performance synaptic devices. Additional efforts are also demanded to enhance the functionality of artificial neurons while maintaining a relatively low cost in area and power, and it will be of significance to explore the intrinsic neuronal stochasticity in computing and optimize their driving capability, etc. Finally, by looking into the correlations between the operation mechanisms, material systems, device structures, and performance, we provide clues to future material selections, device designs, and integrations for artificial synapses and neurons.

A comprehensive review on emerging artificial neuromorphic devices

In this paper, the state of the art in ultra-low power (ULP) VLSI design is presented within a unitary framework for the first time. A few general principles are first introduced to gain an insight into the design issues and the approaches that are specific to ULP systems, as well as to better understand the challenges that have to be faced in the foreseeable future. Intuitive understanding is accompanied by rigorous analysis for each key concept. The analysis ranges from the circuit to the micro-architectural level, and reference is given to process, physical and system levels when necessary. Among the main goals of this paper, it is shown that many paradigms and approaches borrowed from traditional above-threshold low-power VLSI design are actually incorrect. Accordingly, common misconceptions in the ULP domain are debunked and replaced with technically sound explanations.

Ultra-Low Power VLSI Circuit Design Demystified and Explained: A Tutorial

This paper explores the scalability of in-plane and perpendicular MTJ based STT-MRAMs from 65 nm to 8 nm while taking into consideration realistic variability effects. We focus on the read and write performances of a STT-MRAM based cache rather than the obvious advantages such as the denser bit-cell and zero static power. An accurate MTJ macromodel capturing key MTJ properties was adopted for efficient Monte Carlo simulations. For the simulation of access devices and peripheral circuitries, ITRS projected transistor parameters were utilized and calibrated using the MASTAR tool that has been widely used in industry. 6T SRAM and STT-MRAM arrays were implemented with aggressive assist schemes to mimic industrial memory designs. A constant JC0·RA/VDD scaling scenario was used which to the first order gives the optimal balance between read and write margins of STT-MRAMs. The thermal stability factor ensuring a 10 year retention time was obtained by adjusting the free layer thickness as well as assuming improvement in the crystalline anisotropy. Our studies based on the proposed scaling methodology show that in-plane STT-MRAM will outperform SRAM from 15 nm node, while its perpendicular counterpart requires further innovations in MTJ material in order to overcome the poor write performance scaling from 22 nm node onwards.

A Scaling Roadmap and Performance Evaluation of In-Plane and Perpendicular MTJ Based STT-MRAMs for High-Density Cache Memory

Precise measurement of digital circuit degradation is a key aspect of aging tolerant digital circuit design. In this study, we present a fully digital on-chip reliability monitor for high-resolution frequency degradation measurements of digital circuits. The proposed technique measures the beat frequency of two ring oscillators, one stressed and the other unstressed, to achieve 50 X higher delay sensing resolution than that of prior techniques. The differential frequency measurement technique also eliminates the effect of common-mode environmental variation such as temperature drifts between each sampling points. A 265 X 132 mum2test chip implementing this design has been fabricated in a 1.2 V, 130 nm CMOS technology. The measured resolution of the proposed monitoring circuit was 0.02%, as the ring oscillator in this design has a period of 4 ns; this translates to a temporal resolution of 0.8 ps. The 2 mus measurement time was sufficiently short to suppress the unwanted recovery effect from concealing the actual circuit degradation.

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Silicon Odometer: An On-Chip Reliability Monitor for Measuring Frequency Degradation of Digital Circuits

A 2 muW, 100 kHz, 480 kb subthreshold SRAM operating at 0.2 V is demonstrated in a 130 nm CMOS process. A 10-T SRAM cell allows 1 k cells per bitline by eliminating the data-dependent bitline leakage. A virtual ground replica scheme is proposed for logic "0" level tracking and optimal sensing margin in read buffers. Utilizing the strong reverse short channel effect in the subthreshold region improves cell writability and row decoder performance due to the increased current drivability at a longer channel length. The sizing method leads to an equivalent write wordline voltage boost of 70 mV and a delay improvement of 28% in the row decoder compared to the conventional sizing scheme at 0.2 V. A bitline writeback scheme was used to eliminate the pseudo-write problem in unselected columns.

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A 0.2 V, 480 kb Subthreshold SRAM With 1 k Cells Per Bitline for Ultra-Low-Voltage Computing

A 10T SRAM cell with data-independent bitline leakage and a virtual-ground replica scheme allows 1k cells per bitline in subthreshold SRAMs. Reverse short-channel effect is used to improve writability, offer higher speed, reduce junction capacitance, and decrease circuit variability. A 0.13mum, the 480kb SRAM test chip shows a minimum operating voltage of 0.20V.

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Tae-Hyoung Kim

Papers

A Scaling Roadmap and Performance Evaluation of In-Plane and Perpendicular MTJ Based STT-MRAMs for High-Density Cache Memory

Silicon Odometer: An On-Chip Reliability Monitor for Measuring Frequency Degradation of Digital Circuits

A 0.2 V, 480 kb Subthreshold SRAM With 1 k Cells Per Bitline for Ultra-Low-Voltage Computing

A High-Density Subthreshold SRAM with Data-Independent Bitline Leakage and Virtual Ground Replica Scheme

Data-Independent Bitline Leakage and Virtual Ground Replica Scheme