CWC: A Companion Write Cache for Energy-Aware Multi-Level Spin-Transfer Torque RAM Cache Design

Energy efficiency has become one of the primary considerations in the designs of cyber-physical systems (CPS). However, CPS with static RAM (SRAM)-based processors suffers from the high leakage power issue of SRAM, thus limiting the energy efficiency of CPS. Recently, Spin-Transfer Torque RAM (STT-RAM) has emerged and been widely regarded as a great alternative as the on-chip memory within processors, owing to STT-RAM's high density and near-zero leakage power characteristics. In addition, recent advances in Magnetic Tunneling Junction (MTJ) technology also realize the multi-level cell (MLC) STT-RAM to further enhance the memory density. Nevertheless, the write disturbance issue greatly limits the energy efficiency of MLC STT-RAM. Even though studies have been proposed to alleviate this issue, most of the previous disturbance reduction strategies could induce additional management overhead by utilizing counters or cause frequent swap operations when the write disturbance happens. Such observations motivate us to propose a simple and effective solution to unify the direct and split mapping cache designs for improving the energy efficiency of MLC STT-RAM. The proposed design is evaluated through a series of experiments on an emulator with encouraging results.

This paper presents the application of quantum dot gate non-volatile memory (QDNVM) for single-level, multi-level and triple level memory cell. The charge accumulation in the gate region varies the threshold voltage of QDNVM which can be controlled by the application of external gate pulse. More pulse helps to tunnel more charge carriers form the inversion channel to the Quantum dot (QD) layer in the gate region of the QDNVM. The varying stored charge in the QD layer of the QDNVM, varies the threshold voltage of the device. By controlling the applied gate voltage pulse, the QDNVM can be used for single level cell (SLC), multi-level cell (MLC) and triple level Cell (TLC). A simplified circuit model using VHDL is developed for QDNVM. This work will highlight future memory integration using QDNVM.

Conventional DRAM and flash memory are reaching their scaling limits thus motivating research in various emerging memory technologies as a potential replacement. Among these, phase change memory (PCM) has received considerable attention owing to its high scalability and multi-level cell (MLC) operation for high storage density. However, due to the resistance drift over time, the soft error rate in MLC PCM is high. Additionally, the iterative programming in MLC negatively impacts performance and cell endurance. The conventional methods to overcome the drift problem incur large overheads, impact memory lifetime and are inadequate in terms of acceptable soft error rate (SER). In this paper, we propose a new PCM memory architecture with heterogeneous PCM arrays to increase reliability, performance and lifetime. The basic storage unit in the proposed architecture consists of two single-level cells (SLCs) and one four-level cell (4LC). Using the reduced number of 4LCs compared to conventional homogeneous 4LC PCM arrays, the drift-induced error rate is considerably reduced. By alternating each cell operation between SLC and 4LC over time, the overall lifetime can also be significantly enhanced. The proposed architecture achieves up to 105 times lower soft error rate with considerably less ECC overhead. With simple ECC scheme, about 22% performance improvement is achieved and additionally, the overall lifetime is also enhanced by about 57%.

Modern high performance Chip Multiprocessor (CMP) systems rely on large on-chip cache hierarchy. As technology scales down, the leakage power of present SRAM based cache gradually dominates the on-chip power consumption, which can severely jeopardize system performance. The emerging nonvolatile Spin Transfer Torque RAM (STT-RAM) is a promising candidate for large on-chip cache because of the ultra low leakage power. However, the write operations on STT-RAM suffer from considerably higher energy as well as longer latency compared with SRAM which will make STT-RAM in trouble for write-intensive workloads. In this paper, we propose to integrate SRAM with STT-RAM to construct a novel hybrid cache architecture for CMPs. We also propose dedicated microarchitectural mechanisms to make the hybrid cache robust to workloads with different write patterns. Extensive simulation results demonstrate that the proposed hybrid scheme is adaptive to variations of workloads. Overall power consumption is reduced by 37.1% and performance is improved by 23.6% on average compared with SRAM based static NUCA under the same area configuration.

Multi-level cell (MLC) phase-change memory (PCM) is an attractive solution for next-generation memory that is composed of resistance-based nonvolatile devices. MLC PCM is superior to dynamic random-access memory (DRAM) with regard to scalability and leakage power. Therefore, various studies have focused on the feasibility of MLC PCM-based main memory. The key challenges in replacing DRAM with MLC PCM are low reliability, limited lifetime, and long write latency, which are predominantly affected by the most error-vulnerable data pattern. Based on the physical characteristics of the PCM, where the reliability depends on the data pattern, a tri-level-cell (3LC) PCM has significantly higher performance and lifetime than a four-level-cell (4LC) PCM. However, a storage density is limited by binary-to-ternary data mapping. This paper introduces error-vulnerable pattern-aware binary-to-ternary data mapping utilizing 3LC PCM without an error-correction code (ECC) to enhance the storage density. To mitigate the storage density loss caused by the 3LC PCM, a two-way encoding is applied. The performance degradation is minimized through parallel encoding. The experimental results demonstrate that the proposed method improves the storage density by 17.9%. Additionally, the lifetime and performance are enhanced by 36.1% and 38.8%, respectively, compared with those of a 4LC PCM with an ECC.

There is growing interest in emerging non-volatile memory technologies such as Phase-Change Memory, Memristors, and Spin-Transfer Torque RAM (STT-RAM). STT-RAM, in particular, is experiencing rapid development that can be difficult for memory systems researchers to take advantage of. What is needed are techniques that enable designers to explore the potential of recent STT-RAM designs and adjust the performance without needing a detailed understanding of the physics. In this paper, we present the STeTSiMS STT-RAM Simulation and Modeling System to assist memory systems researchers. After providing background on the operation of STT-RAM magnetic tunnel junctions (MTJs), we demonstrate how to fit three different published MTJ models to our model and normalize their characteristics with respect to common metrics. The high-speed switching behavior of the designs is evaluated using macromagnetic simulations. We have also added a first-order model for STT-RAM memory arrays to the CACTI memory modeling tool, which we then use to evaluate the performance, energy consumption, and area for: (i) a high-performance cache, (ii) a high-capacity cache, and (iii) a high-density memory.

Green cloud is an emerging new technology in the computing world in which memory is a critical component. Phase-change memory (PCM) is one of the most promising alternative techniques to the dynamic random access memory (DRAM) that faces the scalability wall. Recent research has been focusing on the multi-level cell (MLC) of PCM. By precisely arranging multiple levels of resistance inside a PCM cell, more than one bit of data can be stored in one single PCM cell. However, the MLC PCM suffers from the degradation of performance compared to the single-level cell (SLC) PCM, due to the longer memory access time. In this paper, we present a genetic-based optimization algorithm for chip multiprocessor (CMP) equipped with PCM memory in green clouds. The proposed genetic-based algorithm not only schedules and assigns tasks to cores in the CMP system, but also provides a PCM MLC configuration that balances the PCM memory performance as well as the efficiency. The experimental results show that our genetic-based algorithm can significantly reduce the maximum memory usage by 76.8 percent comparing with the uniform SLC configuration, and improve the efficiency of memory usage by 127 percent comparing with the uniform 4 bits/cell MLC configuration. Moreover, the performance of the system is also improved by 24.5 percent comparing with the uniform 4 bits/cell MLC configuration in terms of total execution time.

Spin-Transfer Torque RAM (STT-RAM) is an emerging non-volatile memory technology that is a potential universal memory that could replace SRAM in processor caches. This paper presents a novel approach for redesigning STT-RAM memory cells to reduce the high dynamic energy and slow write latencies. We lower the retention time by reducing the planar area of the cell, thereby reducing the write current, which we then use with CACTI to design caches and memories. We simulate quad-core processor designs using a combination of SRAM- and STT-RAM-based caches. Since ultra-low retention STT-RAM may lose data, we also provide a preliminary evaluation for a simple, DRAM-style refresh policy. We found that a pure STT-RAM cache hierarchy provides the best energy efficiency, though a hybrid design of SRAM-based L1 caches with reduced-retention STT-RAM L2 and L3 caches eliminates performance loss while still reducing the energy-delay product by more than 70%.

Artificial intelligence Internet of Things (AIoT) systems need more processing capability and low energy consumption on the terminals. Increasing the cache capacity at the architecture level can effectively reduce the cache missing rate and the number of off-chip memory access, which improves the performance of processors. Because of the limitation of the storage density and high leakage currents, improving SRAM cache capacity will lead to big chip area and high power consumption of processors. Emerging non-volatile memory (NVM), such as spin-transfer torque RAM (STT-RAM), has some characters such as bit width read/write and short read/write latency which are attractive options for replacing or augmenting SRAM. However, the asymmetric read/write latency of NVM brings some new challenges in designing caches. In this paper, we introduce a method to bring STT-RAM into the cache system of AIoT processors. Experimental results show that if we replace SRAM L2 cache with the same sized STT-RAM L2 cache, the out-of-order processor performance is improved up to 10 percent. We first find that for the STT-RAM cache, although the better-performing stream-based data prefetch configuration can improve the processor performance, the amount of prefetching data also increases obviously, which has negative effect on the processor performance because of the cache congestion caused by STT-RAM's long write latency. This paper presents a novel stream-based prefetch method ANCP (Adaptive Non-volatile Cache Prefetch) to reduce the amount of prefetch-write on STT-RAM. Compared with the best-performing stream-based data prefetch configuration, ANCP reduces the prefetching issued by 11 percent on average, which obtains additional up to 8 percent performance improvement of the AIoT processor.

This research aims to design a new phase-change RAM (PRAM)-based main memory structure, supporting the advantages of PRAM while providing performance similar to that of conventional DRAM main memory. To replace conventional DRAMs with non-volatile PRAMs as the main memory components, comparable memory access latency, overall cost, power dissipation, and memory cell endurance should be supported. For these goals, we propose a new main memory system consisting of a DRAM converter and an array of single-level cell (SLC)/multi-level cell (MLC) PRAMs. The DRAM converter consists of an aggressive fetching superblock buffer to assure better use of spatial locality and a selective filtering buffer for better use of temporal locality. The array of the SLC/MLC hybrid PRAM structure includes a combination of SLC and MLC PRAMs to enhance the lifetime of the MLC PRAM main memory and hide asymmetric read/write access latency. The proposed structure is evaluated by a trace-driven simulator using SPEC CPU 2006 and SPLASH-2 traces. Experimental results show that the proposed DRAM converter can reduce the miss rate by ∼37% and write count by ∼55% in comparison with the uniform buffer case. Also, the SLC/MLC PRAM with DRAM converter shows performance in terms of access latency and power consumption close to that of the conventional memory architecture. Thus, our proposed memory architecture can be used to replace the current DRAM-based main memory system.

Spin Torque Transfer RAM (STT-RAM), a assuring possible choice of Static RAM for diminishing leakage power utilization. STT-RAM has the resistance of the MTJ (Magnetic Tunnel Junction) that changes based on stored data. By using differential sensing with reconfigurable cache cell, the STT-RAM performance can be improved. This scheme largely reduces the read disturbance error and average power dissipation with small reading current. Furthermore reducing the power dissipation of STT-RAM, we introduce one new approach as Adiabatic (Energy recovery) Technique. Adiabatic Technique main motive is to reduce the average power dissipation with no performance degradation. The Adiabatic logic and memory are attractive solution for low power application.

A compiler assisted wear leveling for morphable PCM in embedded systems

Phase change memory (PCM) is considered as a promising alternative of DRAM-based main memory in embedded systems. A PCM cell can be dynamically programmed to be in either multiple-level cell (MLC) mode or single-level cell (SLC) mode. With this morphable feature, we can utilize the high-density of MLC and low-latency of SLC, to satisfy various memory requirements of specific applications in embedded systems. However, compared to its SLC counterpart, the lifetime of MLC is limited. In particular, a program in embedded systems usually exhibits an extremely unbalanced write pattern, which may accelerate the wear-out of MLC cells in morphable PCM. To address this issue, this paper proposes a simple and effective wear-leveling technique, named Mixer, to enhance the lifetime of morphable PCM considering the program specific features. We first build an Integer Linear Programming (ILP) formulation to produce optimal SLC/MLC partition and data allocation, to achieve a balanced write distribution in morphable PCM with low memory access cost. The basic idea is to allocate fast SLC and MLC cells for write intensive variables and other ordinary variables, respectively. We then propose a polynomial time algorithm to achieve near-optimal results. The evaluation results show that the proposed technique can effectively extend the lifetime of morphable PCM in embedded systems compared with previous work.

TAMPER presents a novel write-energy-aware driver circuit to decrease Write Error Rate (WER). Complementary metal-oxide-semiconductor, abbreviated as CMOS are vulnerable to serious shortage in designing parameters such as leakage power, scalability, and vulnerability to soft errors for designing a high dencity circuits. Spin Transfer Torque RAM (STT-RAM) is known as the most promising candidate for replacement the current memory technologies according to non-volatility characteristic. write errors occur in this technology primarily because of the process variation phenomenon. Write errors associated with different current densities is one of the major drawbacks of STT-RAM, also seen elsewhere in similar devices and a possible solution to such is presented and discussed in TAMPER. A thermal assistant write driver circuit is proposed, which has the ability to eliminate the process variation on write operation. By using this thermal assistance the asymmetric behavior of Magnetic Tunneling Junction (MTJ) over writing two states "0" and "1" decrease, which leads to reduce the time and energy needed for the writing operation. Experimental simulations have been done and compared with existing methodologies to validate the design. Power, performance, and WER enhancement by optimization in physical characteristics of transistors is achieved with on average 38.68% improvement of write time latency along with a 1.41% decrease in area.

This paper reports the performance and endurance impacts of a slow-write approach for a multi-level cell (MLC) of phase change memory (PCM). An MLC improves the density of PCM, but the endurance is a critical issue. To extend the lifetime of the cell, a slow-write approach is one of the techniques that is used. However, the slow-write approach increases the program execution time because it takes a long time. In this paper, we discuss three types of slow-write approach for MLC and evaluate the endurance and performance quantitatively to understand the effectiveness of our approach. Our evaluation results show that one of the approaches enhances the endurance of MLC PCM 1.57 times with a 1.41 % performance degradation on average compared with the conventional write operation.