A Low-Power,Single-Poly,Non-Volatile Memory for Passive RFID Tags

Single-poly,576bit non-volatile memory is designed and implemented in an SMIC 0.18μm standard CMOS process for the purpose of reducing the cost and power of passive RFID tag chips. The memory bit cell is designed with conventional single-poly pMOS transistors, based on the bi-directional Fowler-Nordheim tunneling effect, and the typical program/erase time is 10ms for every 16bits. A new ,single-ended sense amplifier is proposed to reduce the power dissipation in the current sensing scheme. The average current consumption of the whole memory chip is 0.8μA for the power supply voltage of 1.2V at a reading rate of 640kHz.

Overview of one transistor type of hybrid organic ferroelectric non-volatile memory

Organic ferroelectric memory devices based on field effect transistors that can be configured between two stable states of on and off have been widely researched as the next generation data storage media in recent years.This emerging type of memory devices can lead to a new instrument system as a potential alternative to previous non-volatile memory building blocks in future processing units because of their numerous merits such as cost-effective process,simple structure and freedom in substrate choices.This bi-stable non-volatile memory device of information storage has been investigated using several organic or inorganic semiconductors with organic ferroelectric polymer materials.Recent progresses in this ferroelectric memory field,hybrid system have attracted a lot of attention due to their excellent device performance in comparison with that of all organic systems.In this paper,a general review of this type of ferroelectric non-volatile memory is provided,which include the device structure,organic ferroelectric materials,electrical characteristics and working principles.We also present some snapshots of our previous study on hybrid ferroelectric memories including our recent work based on zinc oxide nanowire channels.

Formation of high density TiN nanocrystals and its application in non-volatile memories

Non-volatile memory based on TiN nanocrystal （TiN-NC） charge storage nodes embedded in SiO2 has been fabricated and its electrical properties have been measured. It was found that the density and size distribution of TiN-NCs can be controlled by annealing temperature. The formation of well separated crystalline TiN nano-dots with an average size of 5 nm is confirmed by transmission electron microscopy and x-ray diffraction, x-ray photoelectron spectroscopy confirms the existence of a transition layer of TiNxOy/SiON oxide between TiN-NC and SiO2, which reduces the barrier height of tunnel oxide and thereby enhances programming/erasing speed. The memory device shows a memory window of 2.5V and an endurance cycle throughout 10^5. Its charging mechanism, which is interpreted from the analysis of programming speed （dVth/dt） and the gate leakage versus voltage characteristics （Ig vs Vg）, has been explained by direct tunnelling for tunnel oxide and Fowler Nordheim tunnelling for control oxide at programming voltages lower than 9V, and by Fowler-Nordheim tunnelling for both the oxides at programming voltages higher than 9V.

Molecular dynamics simulations on the wet/dry self-latching and electric fields triggered wet/dry transitions between nanosheets:A non-volatile memory nanostructure

We design a nanostructure composing of two nanoscale graphene sheets parallelly immersed in water.Using molecular dynamics simulations,we demonstrate that the wet/dry state between the graphene sheets can be self-latched;moreover,the wet→dry/dry→wet transition takes place when applying an external electric field perpendicular/parallel to the graphene sheets(E;/E;).This structure works like a flash memory device(a non-volatile memory):the stored information(wet and dry states)of the system can be kept spontaneously,and can also be rewritten by external electric fields.On the one hand,when the distance between the two nanosheets is close to a certain distance,the free energy barriers for the transitions dry→wet and wet→dry can be quite large.As a result,the wet and dry states are self-latched.On the other hand,an E;and an E;will respectively increase and decrease the free energy of the water located in-between the two nanosheets.Consequently,the wet→dry and dry→wet transitions are observed.Our results may be useful for designing novel information memory devices.

An Overview of Non-Volatile Flip-Flops Based on Emerging Memory Technologies(Invited paper)

Low power consumption is a major issue in nowadays electronics systems. This trend is pushed by the development of data center related to cloud services and soon to the Internet of Things （IoT） deployment. Memories are one of the major contributors to power consumption. However, the development of emerging memory technologies paves the way to low-power design, through the partial replacement of the dynamic random access memory （DRAM） with the non-volatile stand-alone memory in servers or with the embedded or distributed emerging non-volatile memory in IoT objects. In the latter case, non-volatile flip-flops （NVFFs） seem a promising candidate to replace the retention latch. Indeed, IoT objects present long sleep time and NVFFs offer to save data in registers with zero power when the application is idle. This paper gives an overview of NVFF architecture flavors for various emerging memory technologies.

Design of an Electrically Written and Optically Read Non-volatile Memory Device Employing BiFeO3/Au Heterostructures with Strong Absorption Resonance

Exploiting new concepts for dense, fast, and nonvolatile random access memory with reduced energy consump- tion is a significant issue for information technology. Here we design an ＇electrically written and optically read＇ information storage device employing BiFeO3/A u heterostruetures with strong absorption resonance. The electro- optic effect is the basis for the device design, which arises from the strong absorption resonance in BiFeO3/Au heterostructures and the electrically tunable significant birefringence of the BiFeO3 film. We first construct a sim- ulation calculation of the BiFeO3/Au structure spectrum and identify absorption resonance and electro-optical modulation characteristics. Following a micro scale partition, the surface reflected light intensity of different polarization units is calculated. The results depend on electric polarization states of the BiFeO3 film, thus BiFeO3/Au heterostructures can essentially be designed as a type of electrically written and optically read infor- mation storage device by utilizing the scanning near-field optical microscopy technology based on the conductive silicon cantilever tip with nanofabricated aperture. This work will shed light on information storage technology.

Power-Aware Data Management Based on Hybrid RAM-NVM Memory for Smart Bracelet

Wearable devices become popular because they can help people observe health condition.The battery life is the critical problem for wearable devices. The non-volatile memory(NVM) attracts attention in recent years because of its fast reading and writing speed, high density, persistence, and especially low idle power. With its low idle power consumption,NVM can be applied in wearable devices to prolong the battery lifetime such as smart bracelet. However, NVM has higher write power consumption than dynamic random access memory(DRAM). In this paper, we assume to use hybrid random access memory(RAM)and NVM architecture for the smart bracelet system.This paper presents a data management algorithm named bracelet power-aware data management(BPADM) based on the architecture. The BPADM can estimate the power consumption according to the memory access, such as sampling rate of data, and then determine the data should be stored in NVM or DRAM in order to satisfy low power. The experimental results show BPADM can reduce power consumption effectively for bracelet in normal and sleeping modes.

Fabrication and integration of photonic devices for phase-change memory and neuromorphic computing

In the past decade,there has been tremendous progress in integrating chalcogenide phase-change materials(PCMs)on the silicon photonic platform for non-volatile memory to neuromorphic in-memory computing applications.In particular,these non von Neumann computational elements and systems benefit from mass manufacturing of silicon photonic integrated circuits(PICs)on 8-inch wafers using a 130 nm complementary metal-oxide semiconductor line.Chip manufacturing based on deep-ultraviolet lithography and electron-beam lithography enables rapid prototyping of PICs,which can be integrated with high-quality PCMs based on the wafer-scale sputtering technique as a back-end-of-line process.In this article,we present an overview of recent advances in waveguide integrated PCM memory cells,functional devices,and neuromorphic systems,with an emphasis on fabrication and integration processes to attain state-of-the-art device performance.After a short overview of PCM based photonic devices,we discuss the materials properties of the functional layer as well as the progress on the light guiding layer,namely,the silicon and germanium waveguide platforms.Next,we discuss the cleanroom fabrication flow of waveguide devices integrated with thin films and nanowires,silicon waveguides and plasmonic microheaters for the electrothermal switching of PCMs and mixed-mode operation.Finally,the fabrication of photonic and photonic–electronic neuromorphic computing systems is reviewed.These systems consist of arrays of PCM memory elements for associative learning,matrix-vector multiplication,and pattern recognition.With large-scale integration,the neuromorphic photonic computing paradigm holds the promise to outperform digital electronic accelerators by taking the advantages of ultra-high bandwidth,high speed,and energy-efficient operation in running machine learning algorithms.

A Low Power Non-Volatile LR-WPAN Baseband Processor with Wake-Up Identification Receiver

The paper proposes a low power non-volatile baseband processor with wake-up identification(WUI) receiver for LR-WPAN transceiver.It consists of WUI receiver,main receiver,transmitter,non-volatile memory(NVM) and power management module.The main receiver adopts a unified simplified synchronization method and channel codec with proactive Reed-Solomon Bypass technique,which increases the robustness and energy efficiency of receiver.The WUI receiver specifies the communication node and wakes up the transceiver to reduce average power consumption of the transceiver.The embedded NVM can backup/restore the states information of processor that avoids the loss of the state information caused by power failure and reduces the unnecessary power of repetitive computation when the processor is waked up from power down mode.The baseband processor is designed and verified on a FPGA board.The simulated power consumption of processor is 5.1uW for transmitting and 28.2μW for receiving.The WUI receiver technique reduces the average power consumption of transceiver remarkably.If the transceiver operates 30 seconds in every 15 minutes,the average power consumption of the transceiver can be reduced by two orders of magnitude.The NVM avoids the loss of the state information caused by power failure and energy waste caused by repetitive computation.

支持高并发访问的新型NVM存储系统

I/O系统软件栈是影响NVM存储系统性能的重要因素。针对NVM存储系统的读写速度不均衡、写寿命有限等问题,设计了同异步融合的访问请求管理策略;在使用异步策略管理数据量较大的写操作的同时,仍然使用同步策略管理读请求和少量数据的写请求。针对多核处理器环境下不同计算核心访问存储系统时地址转换开销大的问题,设计了面向多核处理器地址转换缓存策略,减少地址转换的时间开销。最后实现了支持高并发访问NVM存储系统(CNVMS)的原型,并使用通用测试工具进行了随机读写、顺序读写、混合读写和实际应用负载的测试。实验结果表明,与PMBD相比,所提策略能提高1%～22%的读写速度和9%～15%的IOPS,验证了CNVMS策略能有效提高NVM存储系统的I/O性能和访问请求处理速度。

嵌入式Flash Memory Cell技术

本文分析了目前常用的快闪存储器(Flash Memory)存储单元结构,介绍了一种适用于嵌入的单元结构,存储器阵列设计、可靠性设计技术。

一种支持大页的层次化DRAM/NVM混合内存系统

随着大数据应用的涌现,计算机系统需要更大容量的内存以满足大数据处理的高时效性需求.新型非易失性存储器(non-volatile memory,NVM)结合传统动态随机存储器(dynamic random access memory,DRAM)组成的混合内存系统具有内存容量大、功耗低的优势,因而得到了广泛关注.大数据应用同时也面临着旁路转换缓冲器(translation lookaside buffer,TLB)缺失率过高的性能瓶颈.大页可以有效降低TLB缺失率,然而,在混合内存中支持大页面临着大页迁移开销过大的问题.因此,设计了一种支持大页和大容量缓存的层次化混合内存系统:DRAM和NVM分别使用4KB和2MB粒度的页面分别进行管理,同时在DRAM和NVM之间实现直接映射.设计了基于访存频率的DRAM缓存数据过滤机制,减轻了带宽压力.提出了基于内存实时信息的动态热度阈值调整策略,灵活适应应用访存特征的变化.实验显示:与使用大页的全NVM内存系统和缓存热页(caching hot page,CHOP)系统相比平均有69.9%和15.2%的性能提升,而与使用大页的全DRAM内存系统相比平均只有8.8%的性能差距.

Approximate Similarity-Aware Compression for Non-Volatile Main Memory

Image bitmaps,i.e.,data containing pixels and visual perception,have been widely used in emerging applica-tions for pixel operations while consuming lots of memory space and energy.Compared with legacy DRAM(dynamic ran-dom access memory),non-volatile memories(NVMs)are suitable for bitmap storage due to the salient features of high density and intrinsic durability.However,writing NVMs suffers from higher energy consumption and latency compared with read accesses.Existing precise or approximate compression schemes in NVM controllers show limited performance for bitmaps due to the irregular data patterns and variance in bitmaps.We observe the pixel-level similarity when writing bitmaps due to the analogous contents in adjacent pixels.By exploiting the pixel-level similarity,we propose SimCom,an approximate similarity-aware compression scheme in the NVM module controller,to efficiently compress data for each write access on-the-fly.The idea behind SimCom is to compress continuous similar words into the pairs of base words with runs.The storage costs for small runs are further mitigated by reusing the least significant bits of base words.SimCom adaptively selects an appropriate compression mode for various bitmap formats,thus achieving an efficient trade-off be-tween quality and memory performance.We implement SimCom on GEM5/zsim with NVMain and evaluate the perfor-mance with real-world image/video workloads.Our results demonstrate the efficacy and efficiency of our SimCom with an efficient quality-performance trade-off.

基于高性能SOC FPGA阵列的NVM验证架构设计与验证

新型非易失性存储器(non-volatile memory,NVM)技术日渐成熟,延迟越来越低,带宽越来越高,未来将不仅有可能取代以动态随机存储器(dynamic random access memory,DRAM)为代表的易失型存储设备在主存中的垄断地位,还有可能取代传统Flash和机械硬盘作为外存服务未来的计算机系统.如何综合各类新型存储的特性,设计高能效的存储架构,实现可应对大数据、云计算所需求的新型主存系统已经成为工业界和学术界的研究热点.提出基于高性能SOC FPGA阵列的NVM验证架构,互联多级FPGA,利用多层次FPGA结构扩展链接多片NVM.依据所提出的验证架构,设计了基于多层次FPGA的主从式NVM控制器,并完成适用于该架构的硬件原型设计.该架构不仅可以实现测试同类型多片NVM协同工作,也可以进行混合NVM存储管理方案验证.

面向NVM存储系统的快速文件访问系统

NVM存储设备系统具备提供高吞吐的潜质,包括接近内存的读写速度、字节寻址特性和支持多路转发等优势。但现有的系统软件栈并没有针对NVM去设计,使得系统软件栈存在许多影响系统访问性能的因素。通过分析发现文件系统的锁机制具有较大的开销,这使得数据的并发访问在多核心环境下成为一个难题。为了缓解这些问题,设计了无锁的文件读写机制以及基于字节的读写接口。通过取消基于文件的锁机制改变了粗粒度的访问控制,利用自主管理请求提高了进程的并发度;在设计能够利用字节寻址的新的文件访问接口时,不仅考虑了NVM存储设备的读写非对称,还考虑了其读写操作的不同特性。这些设计减少了软件栈的开销,有利于发挥NVM特性来提供一个高并发、高吞吐和耐久的存储系统。最后利用开源NVM模拟器PMEM实现了FPMRW原型系统,使用Filebench通用测试工具对FPMRW进行测试与分析,结果显示,FPMRW相对EXT+PMEM和XFS+PMEM能提高3%~40%的系统吞吐率。

In-memory computing to break the memory wall

Facing the computing demands of Internet of things(IoT)and artificial intelligence(AI),the cost induced by moving the data between the central processing unit(CPU)and memory is the key problem and a chip featured with flexible structural unit,ultra-low power consumption,and huge parallelism will be needed.In-memory computing,a non-von Neumann architecture fusing memory units and computing units,can eliminate the data transfer time and energy consumption while performing massive parallel computations.Prototype in-memory computing schemes modified from different memory technologies have shown orders of magnitude improvement in computing efficiency,making it be regarded as the ultimate computing paradigm.Here we review the state-of-the-art memory device technologies potential for in-memory computing,summarize their versatile applications in neural network,stochastic generation,and hybrid precision digital computing,with promising solutions for unprecedented computing tasks,and also discuss the challenges of stability and integration for general in-memory computing.

BOOM-KV:基于RDMA的高性能NVM键值数据库

随着英特尔傲腾数据中心持久化内存模块(DCPMM)开始进入市场以及远程直接内存访问(RDMA)硬件成本的降低,设计融合非易失性内存(NVM)和RDMA的键值(KV)数据库面临新的机遇和挑战。构建基于NVM和RDMA的KV数据库的关键在于设计一个高效的通信协议。遗憾的是,现有工作或采用NVM不感知的RDMA协议,或采用低效的NVM感知的RDMA协议,这导致它们无法最大化KV数据库的性能。本文提出了BOOM协议——一种新型的NVM感知的RDMA协议。相较于NVM不感知的协议,BOOM协议允许直接对远端NVM进行RDMA操作,消除了冗余的数据拷贝;相较于现有的NVM感知的协议,它可以显著减少元数据请求,降低KV请求的端对端延迟。在BOOM协议的基础上构建了BOOM-KV,并针对服务端中央处理器(CPU)利用率和宕机持久化等问题进一步进行优化。将BOOM-KV与最新的研究成果进行对比,结果表明,BOOM-KV能显著降低请求延迟,其中PUT延迟最大降低了42%,GET延迟最大降低了41%,并且展现出良好的扩展性。

非易失性内存友好的线性哈希索引——NVM-LH

非易失性内存(NVM)因其大容量、持久化、按位存取和读延迟低等特性而受到人们的关注,但它同时也具有写次数有限、读写速度不均衡等缺点。针对传统线性哈希索引直接在NVM上实现时会导致大量的随机写操作这一问题,提出了一种新的NVM友好的线性哈希索引NVM-LH。NVM-LH通过存储数据时的缓存行对齐实现了缓存友好性,同时提出了无日志的数据一致性保证策略。此外,NVM-LH还通过优化分裂和删除操作来减少NVM写操作。实验结果表明,NVM-LH在空间利用率上比CCEH高30%,在NVM写次数上比CCEH减少了15%左右,表现了更好的NVM友好性。

A Survey of System Software Techniques for Emerging NVMs

The challenges of power consumption and memory capacity of computers have driven rapid development on non-volatile memories(NVM).NVMs are generally faster than traditional secondary storage devices,write persistently and many offer byte addressing capability.Despite these appealing features,NVMs are difficult to manage and program,which makes it hard to use them as a drop-in replacement for dynamic random-access memory(DRAM).Instead,a majority of modern systems use NVMs through the IO and the file system abstractions.Hiding NVMs under these interfaces poses challenges on how to exploit the new hardware’s performance potential in the existing system software framework.In this article,we survey the key technical issues arisen in this area and introduce several recently developed systems each of which offers novel solutions around these issues.

Utilization-Aware Data Variable Allocation on NVM- Based SPM in Real-Time Embedded Systems

With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been increased.This paper presents a data variable allocation(DVA)algorithm based on the genetic algorithm for NVM-based SPM to prolong the lifetime.The lifetime can be formulated indirectly as the write counts on each SPM address.Since the differences between global variables and stack variables,our optimization model has three constraints.The constraints of the central processing unit(CPU)utilization and size are used for all variables,while no-overlay constraint is only used for stack variables.To satisfy the constraints of the optimization model,we use the greedy strategy to generate the initial population which can determine whether data variables are allocated to SPM and distribute them evenly on SPM addresses.Finally,we use the Mälardalen worst case executive time(WCET)benchmark to evaluate our algorithm.The experimental results show that the DVA algorithm can not only obtain close-to-optimal solutions,but also prolong the lifetime by 9.17% on average compared with SRAM-based SPM.