Binary Neural Networks (BNNs) have been recently proposed to improve the area-/energy-efficiency of the machine/deep learning hardware accelerators, which opens an opportunity to use the technologically more mature binary RRAM devices to effectively implement the binary synaptic weights. In addition, the binary neuron activation enables using the sense amplifier instead of the analog-to-digital converter to allow bitwise communication between layers of the neural networks. However, the sense amplifier has intrinsic offset that affects the threshold of binary neuron, thus it may degrade the classification accuracy. In this work, we analyze a fully parallel RRAM synaptic array architecture that implements the fully connected layers in a convolutional neural network with (+1, -1) weights and (+1, 0) neurons. The simulation results with TSMC 65 nm PDK show that the offset of current mode sense amplifier introduces a slight accuracy loss from ~98.5% to ~97.6% for MNIST dataset. Nevertheless, the proposed fully parallel BNN architecture (P-BNN) can achieve 137.35 TOPS/W energy efficiency for the inference, improved by ~20X compared to the sequential BNN architecture (S-BNN) with row-by-row read-out scheme. Moreover, the proposed P-BNN architecture can save the chip area by ~16% as it eliminates the area overhead of MAC peripheral units in the S-BNN architecture.

Neuro-inspired architectures based on synaptic memory arrays have been proposed for on-chip acceleration of weighted sum and weight update in machine/deep learning algorithms. In this paper, we developed NeuroSim, a circuit-level macro model that estimates the area, latency, dynamic energy, and leakage power to facilitate the design space exploration of neuro-inspired architectures with mainstream and emerging device technologies. NeuroSim provides flexible interface and a wide variety of design options at the circuit and device level. Therefore, NeuroSim can be used by neural networks (NNs) as a supporting tool to provide circuit-level performance evaluation. With NeuroSim, an integrated framework can be built with hierarchical organization from the device level (synaptic device properties) to the circuit level (array architectures) and then to the algorithm level (NN topology), enabling instruction-accurate evaluation on the learning accuracy as well as the circuit-level performance metrics at the run-time of online learning. Using multilayer perceptron as a case-study algorithm, we investigated the impact of the “analog” emerging nonvolatile memory (eNVM)'s “nonideal” device properties and benchmarked the tradeoffs between SRAM, digital, and analog eNVM-based architectures for online learning and offline classification.

Memristor-based computation provides a promising solution to boost the power efficiency of the neuromorphic computing system. However, a behavior-level memristor-based neuromorphic computing simulator, which can model the performance and realize an early stage design space exploration, is still missing. In this paper, we propose a simulation platform for the memristor-based neuromorphic system, called MNSIM. A hierarchical structure for memristor-based neuromorphic computing accelerator is proposed to provides flexible interfaces for customization. A detailed reference design is provided for large-scale applications. A behavior-level computing accuracy model is incorporated to evaluate the computing error rate affected by interconnect lines and nonideal device factors. Experimental results show that MNSIM achieves over 7000 times speed-up than SPICE simulation. MNSIM can optimize the design and estimate the tradeoff relationships among different performance metrics for users.

In this paper, we propose a novel design of strong Physical Unclonable Function (PUF) leveraging the sneak paths in the resistive cross-point (X-point) array for device authentication. The entanglement of the sneak paths in the X-point array greatly enhances the entropy of the physical system, thereby increasing the space of challenge-response pairs (CRPs). “Digital” resistance distribution in X-point array is employed to eliminate the undesired collision or diffuseness when “analog” resistance distribution is used. The X-point PUF's characteristics are evaluated on the 128×128 cross-point arrays by SPICE simulation. The simulation results show that the on-state resistance (Ron) activity affects the PUF's performance and 4% is shown as an optimized design in terms of large reference current (Iref) tolerance range. In addition, the X-point PUF presents no correlation between challenges and responses, showing improved security.

In this paper, we study the 1-selector1-resistor (1S1R) cross-point resistive random access memory (ReRAM) array because of its high density, fast access time, and ultralow stand-by power. Specifically, we focus on an access scheme where a data line is parallelly accessed from multiple subarrays with multibits accessed per subarray. A direct implementation of such a scheme has high energy efficiency but lower reliability compared with a single bit per subarray baseline scheme. So this paper proposes a low cost multilayer approach to improve energy-efficiency of multibits per access scheme without compromising reliability. At the cell level, we show how proper choices of bit-line and source-line voltage and SET recovery help reduce error rate by ten times. At the system level, we propose a new rotated multiarray access scheme where the average error rate of every accessed data line is one order of magnitude lower than the worst case, making it possible to achieve block failure rate of 10-10 with a simple Bose, Chaudhuri, and Hocquenghem t = 4 code. We show that for a 1 GB 1S1R ReRAM, the proposed approach can reduce energy by 41% with 2% extra area while maintaining latency and reliability compared with the baseline system.

Inherent stochastic physical mechanisms in emerging nonvolatile memories (NVMs), such as resistive random-access-memory (RRAM), have recently been explored for hardware security applications. Unlike the conventional silicon Physical Unclonable Functions (PUFs) that are solely based on manufacturing process variation, RRAM has some intrinsic randomness in its physical mechanisms that can be utilized as entropy sources; for instance, resistance variation, random telegraph noise, and probabilistic switching behaviors. This paper reviews the challenges and opportunities in building security primitives with emerging devices. In particular, it presents research progress of RRAM-based hardware security primitives, including PUF and True Random Number Generator (TRNG).

The phenomenon of metal-insulator-transition (MIT) in strongly correlated oxides, such as NbO2, have shown the oscillation behavior in recent experiments. In this work, the MIT based two-terminal device is proposed as a compact oscillation neuron for the parallel read operation from the resistive synaptic array. The weighted sum is represented by the frequency of the oscillation neuron. Compared to the complex CMOS integrate-and-fire neuron with tens of transistors, the oscillation neuron achieves significant area reduction, thereby alleviating the column pitch matching problem of the peripheral circuitry in resistive memories. Firstly, the impact of MIT device characteristics on the weighted sum accuracy is investigated when the oscillation neuron is connected to a single resistive synaptic device. Secondly, the array-level performance is explored when the oscillation neurons are connected to the resistive synaptic array. To address the interference of oscillation between columns in simple cross-point arrays, a 2-transistor-1-resistor (2T1R) array architecture is proposed at negligible increase in array area. Finally, the circuit-level benchmark of the proposed oscillation neuron with the CMOS neuron is performed. At single neuron node level, oscillation neuron shows >12.5× reduction of area. At 128×128 array level, oscillation neuron shows a reduction of ∼4% total area, >30% latency, ∼5× energy and ∼40× leakage power, demonstrating its advantage of being integrated into the resistive synaptic array for neuro-inspired computing.

Memory array architectures have been proposed for on-chip acceleration of weighted sum and weight update in the neuro-inspired machine learning algorithms. As the learning algorithms usually operate on a large weight matrix size, an efficient mapping of a large weight matrix on the hardware accelerator may require partitioning the matrix into multiple sub-arrays. In this work, we built a circuit-level macro simulator to evaluate the performance of partitioning a 512×512 weight matrix into the SRAM and RRAM based accelerators. Generally, with more partitioning and finer granularity of the array architecture, the read/write latency and the dynamic read/write energy will decrease due to an increased computation parallelism at the expense of larger area and leakage power, as shown in the case of the SRAM accelerator. However, the RRAM accelerator does not improve the read latency and read energy beyond a certain partition point because the overhead due to multiple intermediate stages of adders and registers will dominate.

Physical Unclonable Functions (PUF) have to be highly reliable especially when it is being used along with cryptographic hash modules for key generation. To achieve ultrahigh reliability, the conventional approach employs error correction codes (ECC) based on helper data input. Such an approach not only increases the hardware overhead of the PUF but also reduces the entropy of the system, resulting in both hardware and software security issues. In this paper we design a compact and highly reliable PUF architecture based on resistive random access memory (RRAM). We propose a new design where the sum of the read-out currents of multiple RRAM cells is used for generating one response bit. This method statistically minimizes any early-lifetime failure due to RRAM retention degradation at high temperature or under voltage stress. We employ a device model that is calibrated with IMEC HfOx RRAM experimental data and show that with 8 cells per bit, we can ensure99.9999% reliability) for a lifetime >10 years at 125°C. We embed the RRAM PUF into SHA-256 and show that the hardware overhead of the proposed RRAM PUF based architecture is significantly lower than one that uses a traditional RRAM PUF with ECC.

The 3-D integration of resistive switching random access memory (RRAM) array is attractive for low-cost and high-density nonvolatile memory application. In this paper, the design tradeoffs of select transistor drivability, RRAM device characteristics, such as switching current (IW), ON/OFF-state resistance (RON/ROFF), and I-V nonlinearity ratio, interconnect material, and write/read scheme are systematically analyzed using a 3-D circuit simulation. The simulation results show that insufficient current drivability of the vertical transistor severely limits the number of 3-D layers. A low switching current (high RON) or a high nonlinearity is beneficial for improving the write margin, while it degrades the read current sense margin. To alleviate this conflict, the read voltage needs to be boosted to the half write voltage. The common RRAM electrode material TiN is not suitable for the interconnect material due to a high resistivity. To improve write energy efficiency, a multiple-bit write scheme is proposed to reduce the write energy consumption per bit and enable a high bandwidth. With RON= 500 kΩ (IW= 6 μA) and nonlinearity ratio = 10, 1-Mb 3-D vertical RRAM subarray is feasible to meet the specified write/read margin with ~2-pJ/bit energy consumption.

The cross-point array architecture with resistive synaptic devices has been proposed for on-chip implementation of weighted sum and weight update in the training process of learning algorithms. However, the non-ideal properties of the synaptic devices available today, such as the nonlinearity in weight update, limited ON/OFF range and device variations, can potentially hamper the learning accuracy. This paper focuses on the impact of these realistic properties on the learning accuracy and proposes the mitigation strategies. Unsupervised sparse coding is selected as a case study algorithm. With the calibration of the realistic synaptic behavior from the measured experimental data, our study shows that the recognition accuracy of MNIST handwriting digits degrades from ∜97 % to ∜65 %. To mitigate this accuracy loss, the proposed strategies include 1) the smart programming schemes for achieving linear weight update; 2) a dummy column to eliminate the off-state current; 3) the use of multiple cells for each weight element to alleviate the impact of device variations. With the improved synaptic behavior by these strategies, the accuracy increases back to ∜95 %, enabling the reliable integration of realistic synaptic devices in the neuromorphic systems.

The resistive random access memory (RRAM) technology is a promising candidate for the replacement of NAND FLASH at ultra-scaled nodes. In this work, the scalability of a 2-layer RRAM cross-point array architecture is explored towards the 10 nm node. Device-circuit co-design methodologies are employed to optimize the array architecture. The impact of sneak paths, IR drop along the wire interconnect and RRAM device characteristics are investigated by HSPICE simulations and the memory array organization and partition are optimized by NVSim simulations. It is found that increasing the I-V nonlinearity of a memory cell by adding a cell selector helps maintain the write voltage margin at scaled nodes. With the scaling, the maximum sub-array size decreases due to the IR drop constraint thus the memory architecture evolves to a finer granularity. At the 10 nm node, by hiding a portion of the peripheral circuitry underneath the memory array, a 2-layer RRAM bank is projected to achieve ultra-high density ~3.43 Gb/mm2, and can enable fast write bandwith ~ 300 MB/s and read bandwidth ~1 GB/s.

Resistive Random Access Memory (ReRAM) has several advantages over current NAND Flash technology, highlighting orders of magnitude lower access latency and higher endurance. Recently proposed 3D vertical cross-point ReRAM (3D-VRAM) architecture is an encouraging development in ReRAM's evolution as a cost-competitive solution, and thus attracts a lot of attention in both industry and academia. In this work, an array-level model to estimate the read/write energy and characterize the vertical access transistor is developed. We use the model to study a range of design trade-offs by tuning the cell-level characteristics and the read/write schemes. The design space exploration addresses several critical issues that are either unique to 3D-VRAM or have substantially different concerns from the 2D cross-point array design. It provides insights on the design optimizations of the array density and access energy, and several important conclusions have been reached. Then we propose multi-directional write driver to mitigate the writer circuitry overhead, and use remote sensing scheme to take full advantage of limited on-die sensing resources. The benefits of these optimizations are evaluated and validated in our macro-architecture model. With trace-based simulations, system-level comparisons between 3D-VRAM and a wide spectrum of memories are performed in mixed aspects of performance, cost, and energy. The results show that our optimized 3D-VRAM design are better than other contenders for storage memory in both performance and energy.