Deep convolutional neural networks (CNNs) are widely used in Artificial Intelligence of Things (AIoT) systems. Limited by power and area, conventional edge devices are insufficient to handle the cost of CNN computation. The idea of SRAM based Processing-In-Memory (SRAM-PIM) has been advocated to implement CNN on edge devices because of its high area and power efficiency. To further excavate the potential of SRAM-PIM on edge inferences, this paper proposes an SRAM-PIM design with Kullback-Leibler (KL) divergence-based dynamic precision quantization. The proposed quantization method decouples the effect of different CNN layers on accuracy and introduces the SRAM-PIM hardware performance in quantization, realizing SRAM-PIM-aware layer-wise precision adjustment. The proposed SRAM-PIM design has been applied in image classification tasks on edge devices. Our evaluation shows that the implemented design achieves up to 2.03x energy efficiency improvement and 2.54% accuracy improvement compared with existing dynamic precision PIM design. Compared with existing reinforcement-learning-based dynamic quantization method that requires several hours quantization time, the proposed dynamic precision quantization method takes only 26.28us to get the optimal quantization results.

Processing-In-Memory (PIM) technologies is one of most promising candidates for AIoT applications due to its attractive characteristics, such as low computation latency, large throughput and high power efficiency. However, how to efficiently utilize PIM with System-on-Chip (SoC) architecture has been scarcely discussed. In this paper, we demonstrate a series of solution from hardware architecture to algorithm to maximize the benefits of PIM design. First, we propose a Heterogeneous Memory Architecture (HMA) that facilitates the existing SoC with PIM via high-throughput on-chip buses. Then, based on given HMA structure, we also propose an HMA tensor mapping approach to partition tensors and deploy general matrix multiplication operations on PIM structures. Both HMA hardware and HMA tensor mapping approach harnesses the programmability of the mature embedded CPU solution stack and maximize the high efficiency of PIM technology. The whole HMA system can save 416 x power as well as 44.6% design area compare with the latest accelerator solutions. The evaluation also shows that our design can reduce the operation latency by 430 × and 11 × for TinyML applications, compare with state-of-art baseline and PIM without optimization, respectively.

Many cognitive algorithms such as neural networks cannot be efficiently executed by von Neumann architectures, the performance of which is constrained by the memory wall between microprocessor and memory hierarchy. Hence, researchers started to investigate new computing paradigms such as neuromorphic computing that can adapt their structure to the topology of the algorithms and accelerate their executions. New computing units have been also invented to support this effort by leveraging emerging nano-devices. In this work, we will discuss the opportunity of implementing neuromorphic computing systems with spintronic devices. We will also provide insights on how spintronic devices fit into different part of neuromorphic computing systems. Approaches to optimize the circuits are also discussed.

Extreme multi-threading and fast thread switching in modern GPGPU require a large, power-hungry register file (RF), which quickly becomes one of major obstacles on the upscaling path of energy-efficient GPGPU computing. In this work, we propose to implement a power-efficient GPGPU RF built on the newly emerged racetrack memory. Racetrack memory has small cell area, low dynamic power, and nonvolatility. Its unique access mechanism, however, results in a long and location-dependent access latency, which offsets the energy saving benefit it introduces and probably harms the performance. In order to conquer the adverse impacts of racetrack memory based RF designs, we first propose a register mapping scheme to reduce the average access latency. Based on the register mapping, we develop a racetrack memory aware warp scheduling (RMWS) algorithm to further suppress the access latency. RMWS design includes a new write buffer structure that improves the scheduling efficiency as well as energy saving. We also investigate and optimize the design where multiple concurrent RMWS schedulers are employed. Experiment results show that our propose techniques can keep a GPGPU performance similar to the baseline with SRAM based RF while the RF energy is significantly reduced by 48.5 percent.

Rapidly increasing demands for memory capacity and severe technical scaling challenges of conventional memory technologies motivated recent investments on next-generation nonvolatile memory technologies. As a promising candidate, spin-transfer torque random access memory (STT-RAM) has demonstrated many attractive properties, such as nanosecond access time, high integration density, nonvolatility, and excellent CMOS integration compatibility. However, similar to all other nano-devices, the performance and reliability of STT-RAM cells are greatly affected by process variations, device operating uncertainties, and environmental fluctuations. As a result, the read and write operations of STT-RAM demonstrate some variabilities and errors. In this paper, we systematically analyze the impacts of CMOS and magnetic tunneling junction (MTJ) process variations, MTJ resistance switching randomness that are induced by intrinsic thermal fluctuations, and working temperature changes on STT-RAM cell designs. The STT-RAM cell reliability issues in both read and write operations are first investigated. A combined circuit and magnetic simulation platform is then established to quantitatively study the persistent and nonpersistent errors in STT-RAM cell operations. Our analysis proved the importance of a full statistical design method in STT-RAM designs for design pessimism minimization.

Physical Unclonable Functions (PUFs) have been widely proposed as security primitives to provide device identification and authentication. Recently, PUFs based on Non-volatile Memory (NVM) are widely proposed since the promise of NVMs' wide application. In addition, NVM-based PUFs are considered to be more immune to invasive attack and simulation attack than CMOS-based PUFs. However, the existing NVM-based PUF either shows the unreliability under environmental variations or need extra modifications to the IC manufacturing process. In this work, we propose err-PUF, a novel PUF design based on the cell error rate distribution of STT-RAM. Instead of using the distribution directly, we generate a stable fingerprint based on a novel concept called Error-rate Differential Pair (EDP) without modifications to the read/write circuits. Comprehensive results demonstrate that err-PUF can achieve sufficient reliability under environmental variations, which can significantly impact the cell error rates. Moreover, compared with existing approaches, err-PUF has a higher speed and lower power consumption with negligible overhead.

Spin-transfer torque magnetic random access memory (STT-MRAM) is a promising emerging memory technology due to its various advantageous features such as scalability, nonvolatility, density, endurance, and fast speed. However, the reliability of STT-MRAM is severely impacted by environmental disturbances because radiation strike on the access transistor could introduce potential write and read failures for 1T1MTJ cells. In this paper, a comprehensive approach is proposed to evaluate the radiation-induced soft errors spanning from device modeling to circuit level analysis. The simulation based on 3-D metal-oxide-semiconductor transistor modeling is first performed to capture the radiation-induced transient current pulse. Then a compact switching model of magnetic tunneling junction (MTJ) is developed to analyze the various mechanisms of STT-MRAM write failures. The probability of failure of 1T1MTJ is characterized and built as look-up-tables. This approach enables designers to consider the effect of different factors such as radiation strength, write current magnitude and duration time on soft error rate of STT-MRAM memory arrays. Meanwhile, comprehensive write and sense circuits are evaluated for bit error rate analysis under random radiation effects and transistors process variation, which is critical for performance optimization of practical STT-MRAM read and sense circuits.

As manufacture process scales down rapidly, the design of ternary content-addressable memory (TCAM) requiring high storage density, fast access speed and low power consumption becomes very challenging. In recent years, many novel TCAM designs have been inspired by the research on emerging nonvolatile memory technologies, such as magnetic tunneling junction (MTJ), phase change memory (PCM), and memristor. These designs store a data as the resistive variable of a nonvolatile device, which usually results in limited sensing margin and therefore constrains the searching speed of TCAM architecture severely. To further enhance the performance and robustness of TCAMs, we proposed two novel cell designs that utilize MTJs as data storage units - the symmetrical dual-N structure and the asymmetrical P-N scheme. In both designs, a body bias feedback circuit is integrated to enlarge the sensing margins. Compared with an existing MTJ-based TCAM structure, the tolerance in gate voltage variation of the symmetrical dua-N (asymmetrical P-N) scheme can significantly improve 59.5% (21.2%). The latency and the dynamic energy consumption in one searching operation at the word length of 256 bits are merely 590.35ps (97.89ps) and 65.05fJ/bit (36.85fJ/bit), not even mentioning that the use of nonvolatile MTJ devices avoids unnecessary leakage power consumption.

Spin-torque transfer RAM (STT-RAM), a promising alternative to static RAM (SRAM) for reducing leakage power consumption, has been widely studied to mitigate the impact of its asymmetrically long write latency. However, physical effects of technology scaling down to 45 nm and below, in particular, process variation, introduce the previously unreported and alarming trends in read performance and reliability due to reduced sensing margins and increasing error rates. In this brief, we study the scaling trends of STT-RAM from 65 down to 22 nm as they pertain to read performance, including a 50% increase in sensing versus peripheral circuit delay ratio and a more than 80% increase in uncorrectable read error rates. Through differential sensing, we show how 22 nm can return to sense delay ratio levels at 65 nm and uncorrectable read errors can be reduced by an order of magnitude. Through a case study of a multilevel STT-RAM cache, we show how a reconfigurable cache cell can create an extreme access mode (X-mode) based on differential sensing improve to outperform the state-of-the-art STT-RAM caching techniques in both raw performance and performance per watt by more than 10% while still reducing energy consumption over SRAM caches by more than 1/3.

Translation lookaside buffer (TLB) was recently introduced into modern graphics processing unit (GPU) architectures to support virtual memory addressing. Compared to CPUs, the performance of GPUs is more sensitive to the capacity of TLBs because of heavier memory accesses. However, large SRAM cell area greatly limits the implementable capacity of conventional SRAM-based TLBs. In this work, we propose using STT-RAM to construct TLBs in light of the unique memory access pattern in GPUs, i.e., infrequent data updates. STT-RAM TLB can replace its same-area SRAM counterpart with greater capacity, similar read performance and lower energy consumption. As an optimization of STT-RAM TLB, we further propose a STT-RAM-based dynamically-configurable TLB (STD-TLB) by leveraging differential sensing technique. STD-TLB can switch between high-capacity mode and high-performance mode on-the-fly based on real-time application needs. Our experiments show that compared to SRAM TLB, standard STT-RAM TLB improves the performance and energy delay product of GPU address translation by 32% and 75%, respectively, while STD-TLB achieves additional 15% and 13% improvements over standard STT-RAM TLB.

Spin-transfer torque random access memory (STT-RAM) has demonstrated great potentials in embedded and stand-alone applications. However, process variations and thermal fluctuations greatly influence the operation reliability of STT-RAM and limit its scalability. In this work, we propose a new field-assisted access scheme to improve the read/write reliability and performance of STT-RAM. During read operations, an external magnetic field is applied to a magnetic tunneling junction (MTJ) device, generating a resistive sense signal without referring to other devices. Such a self-reference scheme offers a very promising alternative approach to overcome the severe cell-to-cell variations at highly scaled technology node. Furthermore, the external magnetic field can be also used to assist the MTJ switching during write operations without introducing extra hardware overhead. Simulation results show that compared to the existing self-reference scheme, our proposed design can improve the read sense margin by more than 200% and reduce the write error rate down to 2.29×10--9.

SRAM based register file (RF) is one of the major factors limiting the scaling of GPGPU. In this work, we propose to use the emerging nonvolatile domain-wall-shift-write based racetrack memory (DWSW-RM) to implement a power-efficient GPGPU RF, of which the power consumption is substantially reduced. A holistic technology set is developed to minimize the high access cost of DWSW-RW caused by the sequential access mechanism. Experiment results show that our proposed techniques can improve the GPGPU performance by 4.6% compared to the baseline with SRAM based RF. The RF energy efficiency is also significantly improved by 2.45×.

Multi-level Cell Spin-Transfer Torque Random Access Memory (MLC STT-RAM) is a promising nonvolatile memory technology for high-capacity and high-performance applications. However, the reliability concerns and the complicated access mechanism greatly hinder the application of MLC STT-RAM. In this work, we develop a holistic solution set, namely, state-restrict MLC STT-RAM (SR-MLC STT-RAM) to improve the data integrity and performance of MLC STT-RAM with the minimized information density degradation. Three techniques: state restriction (StatRes), error pattern removal (ErrPR), and ternary coding (TerCode) are proposed at circuit level to reduce the read and write errors of MLC STT-RAM cells. State pre-recovery (PreREC) technique is also developed at architecture level to improve the access performance of SR-MLC STT-RAM by eliminating unnecessary two-step write operations. Our simulations show that compared to conventional MLC STT-RAM, SR-MLC STT-RAM can enhance the write and read reliability of memory cells by 10--10000×, allowing the application of simple error correction code schemes. Compared to single-level-cell (SLC) STT-RAM, SR-MLC STT-RAM based cache design can boost the system performance by 6.2% on average by leveraging the increased cache capacity at the same area and the improved write latency.

The development of emerging spin-transfer torque random access memory (STT-RAM) is facing two major technical challenges-poor write reliability and high write energy, both of which are severely impacted by process variations and thermal fluctuations. The evaluations on STT-RAM design metrics and robustness often require a hybrid simulation flow, i.e., modeling the CMOS and magnetic devices with SPICE and macro-magnetic models, respectively. Very often, such a hybrid simulation flow involves expensive Monte Carlo simulations when the design and behavioral variabilities of STT-RAM are taken into account. In this paper, we propose a fast and scalable semi-analytical method-PS3-RAM, enabling efficient statistical simulations in STT-RAM designs. By eliminating the costly macro-magnetic and SPICE simulations, PS3-RAM achieves more than 100\(000\boldsymbol {\times }\) runtime speedup with excellent agreement with the result of conventional simulation method. PS3-RAM can also accurately estimate the STT-RAM write error rate and write energy distributions at both magnetic tunneling junction switching directions under different temperatures, demonstrating great potential in the analysis of STT-RAM reliability and write energy at the early design stage of memory or micro-architecture.

As an emerging nonvolatile memory technology, spin-transfer torque random access memory (STT-RAM) faces great design challenges. The large device variations and the thermal-induced switching randomness of the magnetic tunneling junction (MTJ) introduce the persistent and non-persistent errors in STT-RAM operations, respectively. Modeling these statistical metrics generally require the expensive Monte-Carlo simulations on the combined magnetic-CMOS models, which is hardly integrated in the modern micro-architecture and system designs. Also, the conventional bottom-up design method incurs costly iterations in the STT-RAM design toward specific system requirement. In this work, we propose Loadsa1: a yield-driven top-down design method to explore the design space of STT-RAM array from a statistical point of view. Both array-level semi-analytical yield model and cell-level failure-probability model are developed to enable a top-down design method: The system-level requirements, e.g., the chip yield under power and area constraints, are hierarchically mapped to array-and cell-level design parameters, e.g., redundancy, ECC scheme, and MOS transistor size, etc. Our simulation results show that Loadsa can accurately optimize the STT-RAM based on the system and cell-level constraints with a linear computation complexity. Our method demonstrates great potentials in the early design stage of memory or micro-architecture by eliminating the design integrations, while offering a full statistical view of the design even when the common yield enhancement practices are applied.

Program disturb, read disturb and retention time limit are three major reasons accounting for the bit errors in NAND flash memory. The adoption of multi-level cell (MLC) technology and technology scaling further aggravates this reliability issue by narrowing threshold voltage noise margins and introducing larger device variations. Besides implementing error correction code (ECC) in NAND flash modules, RAID-5 are often deployed at system level to protect the data integrity of NAND flash storage systems (NFSS), however, with significant performance degradation. In this work, we propose a technique called “DA-RAID-5” to improve the performance of the enterprise NFSS under RAID-5 protection without harming its reliability (here DA stands for “disturb aware”). Three schemes, namely, unbound-disturb limiting (UDL), PE-aware RAID-5 and Hybrid Caching(HC) are proposed to protect the NFSS at the different stages of its lifetime. The experimental results show that compared to the best prior work, DA-RAID-5 can improve the NFSS response time by 9.7% on average.

Spin-transfer torque random access memory (STT-RAM) is an emerging non-volatile memory technology offering many attractive characteristics like high integration density, nanosecond access time, and good CMOS compatibility. However, the performance and reliability of STT-RAM cells are greatly affected by process variations and intrinsic device operation randomness. In this work, we proposed a novel STT-RAM cell structure named ADAMS which can be dynamically configured between the high-reliable (HR) mode and the high-capacity (HC) mode upon the real-time system requirement: For the performance and reliability critical applications, ADAMS switches to HR mode. The novel connection scheme of magnetic tunneling junction (MTJ) devices and programming/sensing circuits substantially improve the read and write performance of the ADAMS cell and enhance its resilience to operation errors; For the capacity critical applications, ADAMS switches to HC mode. The ADAMS cell is broken into two “1T1J” cells that can work independently, offering the similar performance and reliability to conventional STT-RAM design. Simulation results show that compared to convectional 1T1J cell structure, ADAMS offers the same write latency, smaller cell area, and 12× lower write error rate. The read latency is also improved by 44.2% with 1935.8× reduction on combined read error rate.

In order to solve the navigation problem in x-ray security inspection, a stereoscopic visualization method is proposed in this paper. By analyzing the geometric relations between x-ray generator, detector and baggage on conveyor belt, a data reorganization method is used to produce rotating views of scanned baggage, which could be used as three-dimensional navigation. For illustration, a single slice helical scan model was adopted to demonstrate the algorithm. Experiment results show that pseudo-three-dimensional navigation is helpful to determine illegal objects in baggage. Compared with two-dimensional digital radiology navigation, this method could provide more information about the scanned object. Compared with real three-dimensional navigation methods with volume render technology, this method has the merit of low computational cost.

Spin-transfer torque random access memory (STT-RAM) has widely believed as a promising candidate for the post-silicon nonvolatile memory technology. In many recent researches, STT-RAM has demonstrated many attractive characteristics, such as nanosecond access time, high integration density, adjustable non-volatility, and good CMOS process compatibility. As the distinction between the two boundary resistance states of the magnetic tunnel junction (MTJ) device continues to improve, multi-level cell (MLC) STT-RAM emerges as an interesting technology to pursue. However, since the resistance margin is partitioned into multiple segments for multi-level data representation, the performance and reliability of MLC STT-RAM cells become more sensitive to the MOS and MTJ device variations, as well as the thermal-induced randomness of MTJ switching. In this work, we report our recent study on the reliability of the read/write operations of the stacking MLC STT-RAM cells by consider the different variability sources. Our simulation result shows that although the stacking MCL STT-RAM has not yet satisfy the requirement of commercial product under the realistic fabrication conditions, it has shown the great potentials under careful design optimizations.

It has been predicted that a processor's caches could occupy as much as 90% of chip area a few technology nodes from the current ones. In this article, we investigate the use of multilevel spin-transfer torque RAM (STT-RAM) cells in the design of processor caches. We start with examining the access (read and write) scheme for multilevel cell (MLC) STT-RAM from a circuit design perspective, detailing the read and write circuits. Compared to traditional SRAM caches, a multilevel cell (MLC) STT-RAM cache design is denser, fast, and requires less energy. However, a number of critical architecture-level issues remain to be solved before MLC STT-RAM technology can be deployed in processor caches. We shall offer solutions to the issue of bit encoding as well as tackle the write endurance problem. In particular, the latter has been neglected in previous works on STT-RAM caches. We propose a set remapping scheme that can potentially prolong the lifetime of a MLC STT-RAM cache by 80× on average. Furthermore, a method for recovering the performance that may be lost in some applications due to set remapping is proposed. The impacts of process variations of the MLC STT-RAM cell on the robustness of the memory hierarchy is also discussed, together with various enhancement techniques, namely, ECC and design redundancy.

Spin-Transfer Torque RAM (STT-RAM), a promising alternative to SRAM for reducing leakage power consumption, has been widely studied to mitigate the impact of its asymmetrically long write latency. Recently, STT-RAM has been proposed for L1 caches by relaxing the data retention time to improve write performance and dynamic energy. However, as the technology scales down from 65nm to 22nm, the performance of the read operation scales poorly due to reduced sense margins and sense amplifier delays. In this article, we leverage a dual-mode STT memory cell to design a configurable L1 cache architecture termed C1C to mitigate read performance barriers with technology scaling. Guided by application access characteristics discovered through novel compiler analyses, the proposed cache adaptively switches between a high performance and a low-power access mode. Our evaluation demonstrates that the proposed cache with compiler guidance outperforms a state-of-the-art STT-RAM cache design by 9% with high dynamic energy efficiency, leading to significant performance/watt improvements over several competing approaches.

Spin-transfer torque random access memory (STT-RAM) is a promising candidate for next-generation non-volatile memory technologies. It combines many attractive attributes such as nanosecond access time, high integration density, non-volatility, and good CMOS process compatibility. However, process variation continues to be a critical issue in the designs of STT-RAM and the derived spintronic logic. Besides the process-variation-induced persistent operation error, the non-persistent error that is incurred by the intrinsic thermal fluctuations of Magnetic Tunneling Junction (MTJ) devices significantly influences the spintronic circuit reliability. In this paper, we analyzed these two types of STT-RAM operation errors at both single cell and array levels. On the top of that, we quantitatively investigate the impacts of these errors on a nonvolatile spintronic flip-flop design. Some possible design techniques to reduce the operation error rate are also discussed. Our experimental results show that a statistical design technique must be adopted in spintronic memory and logic designs to achieve the desired operation reliability. We refer this technique as “probabilistic design”.

Process variations and thermal fluctuations significantly affect the write reliability of spin-transfer torque random access memory (STT-RAM). Traditionally, modeling the impacts of these variations on STT-RAM designs requires expensive Monte-Carlo runs with hybrid magnetic-CMOS simulation steps. In this paper, we propose a fast and scalable semi-analytical simulation method--PS3-RAM, for STT-RAM write reliability analysis. Simulation results show that PS3-RAM offers excellent agreement with the conventional simulation method without running the costly macro-magnetic and SPICE simulations. Our method can accurately estimate the STT-RAM write error rate at both MTJ switching directions under different temperatures while receiving a speedup of multiple orders of magnitude (five order or more). PS3-RAM shows great potentials in the STT-RAM reliability analysis at the early design stage of memory or micro-architecture.

As one promising candidate for next-generation nonvolatile memory technologies, spin-transfer torque random access memory (STT-RAM) has demonstrated many attractive features, such as nanosecond access time, high integration density, non-volatility, and good CMOS process compatibility. In this paper, we reveal an important fact that has been neglected in STT-RAM designs for long: the write operation of a STT-RAM cell is asymmetric based on the switching direction of the MTJ (magnetic tunneling junction) device: the mean and the deviation of the write latency for the switching from low- to high-resistance state is much longer or larger than that of the opposite switching. Some special design concerns, e.g., the write-pattern-dependent write reliability, are raised by this observation. We systematically analyze the root reasons to form the asymmetric switching of the MTJ and study their impacts on STT-RAM write operations. These factors include the thermal-induced statistical MTJ magnetization process, asymmetric biasing conditions of NMOS transistors, and both NMOS and MTJ device variations. We also explore the design space of different design methodologies on capturing the switching asymmetry of different STT-RAM cell structures. Our experiment results proved the importance of full statistical design method in STT-RAM designs for design pessimism minimization.

Spin-transfer torque random access memory (STT-RAM) is a promising nonvolatile memory technology aiming on-chip or embedded applications. In recent years, many researches have been conducted to improve the storage density and enhance the scalability of STT-RAM, such as reducing the write current and switching time of magnetic tunneling junction (MTJ) devices. In parallel with these efforts, the continuous increasing of tunnel magneto-resistance(TMR) ratio of the MTJ inspires the development of multi-level cell (MLC) STT-RAM, which allows multiple data bits be stored in a single memory cell. Two types of MLC STT-RAM cells, namely, parallel MLC and series MLC, were also proposed. The storage margin of a MLC STT-RAM cell, i.e., the distinction between the lowest and highest resistance states, is partitioned into multiple segments for multi-level data representation. As a result, the performance and reliability of MLC STT-RAM cells become more sensitive to the MOS and MTJ device variations and the thermal-induced randomness of MTJ switching. In this work, we systematically analyze the variation sources of MLC STT-RAM designs and their impacts on the reliability of the read and write operations. On top of that, we also discuss the optimal device parameters of the MLC MTJ for the minimization of the operation error rate of the MLC STT-RAM cells from statistical design perspective. Our simulation results show that under the current available technology, series MLC STT-RAM demonstrates overwhelming benefits in the read and write reliability compared to parallel MLC STT-RAM and could potentially satisfy the requirement of commercial practices.

The significant scaling challenges of conventional memories, i.e., SRAM and DRAM, motivated the research on emerging memory technologies. Many promising memory technology candidates, however, suffer from a common issue in their write operations: the switching processes at different write operations (i.e., 0 → 1 and 1 → 0) are asymmetric. Using a pessimistic design corner to cover the worst case of a write operation incurs large power and performance cost in the existing emerging memory technology designs. In this work, we propose a universal log style write methodology to mitigate this asymmetry issue by operating two switching processes in separate stages. The dedicated design optimizations are allowed on either switching process. The simulation results on the spin-transfer-torque random access memory based last-level cache show that our technique can improve the system performance by 4% while receiving 35% power reduction on average1.

In this article, we propose a data storage system with the emerging nonvolatile memory technologies used for the implantable electrocardiography (ECG) recorder. The proposed storage system can record the digitalized real-time ECG waveforms continuously inside the implantable device and export the stored data to external reader periodically to obtain a long-term backup. Spin transfer torque random access memory (STT-RAM) and spintronic memristor are selected as the storage elements for their nonvolatility, high density, high reliability, low power consumption, good scalability, and CMOS technology compatibility. The new read and write schemes of STT-RAM and spintronic memristors are presented and optimized to fit the specific application scenario. The tradeoffs among data accuracy, chip area, and read/write energy for the different technologies are thoroughly analyzed and compared. Our simulation results show the configuration with a data sampling rate (e.g., 128 Hz) and a quantization resolution (e.g., 12 bits) can record 18-hour real-time data within ~ 3.6-mm 2 chip area when the data storage is built with single-level cell (SLC) STT-RAMs. Daily energy consumption is 5.46 mJ. Utilizing the multilevel cell (MLC) STT-RAMs or the spintronic memristors as the storage elements can further reduce the chip area and decrease energy dissipation.

The rapidly increased demands for memory in electronic industry and the significant technical scaling challenges of all conventional memory technologies motivated the researches on the next generation memory technology. As one promising candidate, spin-transfer torque random access memory (STT-RAM) features fast access time, high density, non-volatility, and good CMOS process compatibility. However, like all other nano-scale devices, the performance and reliability of STT-RAM cells are severely affected by process variations, intrinsic device operating uncertainties and environmental fluctuations. In this work, we systematically analyze the impacts of CMOS and MTJ process variations, MTJ switching uncertainties induced by thermal fluctuations and working temperature on the performance and reliability of STT-RAM cells. A combined circuit and magnetic simulation platform is also established to quantitatively analyze the persistent and non-persistent error rates during the STT-RAM cell operations. Finally, an optimization flow and its effectiveness are depicted by using some STT-RAM cell designs as case study.