Emerging computational resistive memory is promising to overcome the challenges of scalability and energy efficiency that DRAM faces and also break through the memory wall bottleneck. However, cell-level and array-level nonideal properties of resistive memory significantly degrade the reliability, performance, accuracy, and energy efficiency during memory access and analog computation. Cell-level nonidealities include nonlinearity, asymmetry, and variability. Array-level nonidealities include interconnect resistance, parasitic capacitance, and sneak current. This review summarizes practical solutions that can mitigate the impact of nonideal device and circuit properties of resistive memory. First, we introduce several typical resistive memory devices with focus on their switching modes and characteristics. Second, we review resistive memory cells and memory array structures, including 1T1R, 1R, 1S1R, 1TnR, and CMOL. We also overview three-dimensional (3D) cross-point arrays and their structural properties. Third, we analyze the impact of nonideal device and circuit properties during memory access and analog arithmetic operations with focus on dot-product and matrix-vector multiplication. Fourth, we discuss the methods that can mitigate these nonideal properties by static parameter and dynamic runtime co-optimization from the viewpoint of device and circuit interaction. Here, dynamic runtime operation schemes include line connection, voltage bias, logical-to-physical mapping, read reference setting, and switching mode reconfiguration. Then, we highlight challenges on multilevel cell cross-point arrays and 3D cross-point arrays during these operations. Finally, we investigate design considerations of memory array peripheral circuits. We also portray an unified reconfigurable computational memory architecture.

Phase Change Memory (PCM) has the potential to replace traditional DRAM memory due to its better scalability and non-volatility. However, PCM also suffers from high write latency and energy consumption. To mitigate the write overhead of PCM-based main memory, we propose a Fine-grained Dirtiness Aware (FDA) last-level cache (LLC) victimization scheme. The key idea of FDA is to preferentially evict cachelines with fewer dirty words when victimizing dirty cachelines. The modified word is defined to be dirty. FDA exploits two key observations. First, the write service time of a cacheline is proportional to the number of dirty words. Second, a cacheline with fewer dirty words has the same or lower reference frequency compared with other dirty cachelines. Therefore, evicting cachelines with fewer dirty words can reduce the write service time of cachelines and will not increase the miss rate. To reduce the write service time of cachelines, FDA evicts the cacheline with the fewest dirty words when victimizing dirty cachelines. We also present FDARP to decrease the miss rate by further synergizing the number of dirty words with Re-reference Prediction Value. Experimental results show that FDA (FDARP) can improve the IPC performance by 8.3% (14.8%), decrease the write service time of cachelines by 37.0% (36.3%) and reduce write energy consumption of PCM by 27.0% (32.5%) under the mixed benchmarks.

Emerging Resistive Random Access Memory (ReRAM) is a promising candidate as the replacement for DRAM due to its low standby power, high density, high scalability, and nonvolatility. By employing the unique crossbar structure, ReRAM can be constructed with extremely high density. However, the crossbar ReRAM faces some serious challenges in terms of performance, reliability, and energy consumption. First, ReRAM’s crossbar structure causes an IR drop problem due to wire resistance and sneak currents, which results in nonuniform access latency in ReRAM banks and reduces its reliability. Second, without access transistors in the crossbar structure, write disturbance results in serious data reliability problem. Third, the access latency, reliability, and energy use of ReRAM arrays are significantly influenced by the data patterns involved in a write operation. To overcome the challenges of the crossbar ReRAM, we propose a novel circuit architecture co-optimization framework for improving the performance, reliability, and energy use of ReRAM-based main memory system, called CACF. The proposed CACF consists of three levels, including the circuit level, circuit architecture level, and architecture level. At the circuit level, to reduce the IR drops along bitlines, we propose a double-sided write driver design by applying write drivers along both sides of bitlines and selectively activating the write drivers. At the circuit architecture level, to address the write disturbance with low overheads, we propose a RESET disturbance detection scheme by adding disturbance reference cells and conditionally performing refresh operations. At the architecture level, a region partition with address remapping method is proposed to leverage the nonuniform access latency in ReRAM banks, and two flip schemes are proposed in different regions to optimize the data patterns involved in a write operation. The experimental results show that CACF improves system performance by 26.1%, decreases memory access latency by 22.4%, shortens running time by 20.1%, and reduces energy consumption by 21.6% on average over an aggressive baseline. Meanwhile, CACF significantly improves the reliability of ReRAM-based memory systems.

The size of write unit in PCM, namely the number of bits allowed to be written concurrently at one time, is restricted due to high write energy consumption. It typically needs several serially executed write units to finish a cache line service when using PCM as the main memory, which results in long write latency and high energy consumption. To address the poor write performance problem, we propose a novel PCM write scheme called Min-WU (Minimize the number of Write Units). We observe data access locality that some frequent zero-extended values dominate the write data patterns in typical multi-threaded applications (more than 40 and 44.9 percent of all memory accesses in PARSEC workloads and SPEC 2006 benchmarks, respectively). By leveraging carefully designed chip-level data redistribution method, the data amount is balanced and the data pattern is the same among all PCM chips. The key idea behind Min-WU is to minimize the number of serially executed write units in a cache line service after data redistribution through sFPC (simplified Frequent Pattern Compression), eRW (efficient Reordering Write operations method) and fWP (fine-tuned Write Parallelism circuits). Using Min-WU, the zero parts of write units can be indicated with predefined prefixes and the residues can be reordered and written simultaneously under power constraints. Our design can improve the performance, energy consumption and endurance of PCM-based main memory with low space and time overhead. Experimental results of 12 multi-threaded PARSEC 2.0 workloads show that Min-WU reduces 44 percent read latency, 28 percent write latency, 32.5 percent running time and 48 percent energy while receiving 32 percent IPC improvement compared with the conventional write scheme with few memory cycles and less than 3 percent storage space overhead. Evaluation results of 8 SPEC 2006 benchmarks demonstrate that Min-WU earns 57.8/46.0 percent read/write latency reduction, 28.7 percent IPC improvement, 28 percent running time reduction and 62.1 percent energy reduction compared with the baseline under realistic memory hierarchy configurations.

The noises at the power lines limit the charge pump to provide large instantaneous current to PCM cells, which results in the number of bits can be written concurrently, i.e. the size of write unit, is restricted in PCM. When implementing PCM as the main memory, the inequality of cache line's size and write unit's size may result in many consecutive executed write units, which greatly decreases the system performance. Existing PCM write schemes, however, consider the worst power and time cases of written data, and ignore the actual current consumption. It is assumed that all data bits are changed and the electric current of each data unit is under fully utilized. The write performance is blocked due to pessimistic estimates, i.e. the current is often excessively supplied but is not used effectively, which leads to huge energy consumption. As a result, the write parallelism is limited and therefore restricts the overall system performance. To address this problem, this paper proposes a novel PCM write scheme named Tetris Write to explore more write parallelism and reduce the critical number of write units in PCM chip. The key idea behind Tetris Write is to monitor the number of '1' and '0' changed in each data unit, and schedule the order of data units' write-1 and write-0 execution considering not only the time and power asymmetries, but also the number asymmetry between RET and SET operations, to allow a larger number of concurrent bit-writes and make the best use of power supply. Tetris Write tries to schedule the dominating long term write-1s first and attempts to steal interspaces remained by write-1s to put the extraessential short write-0s. 4-core PARSEC benchmarks' results show that Tetris Write can get 65% read latency reduction, 40% write latency reduction, 46% running time reduction and 2X IPC improvement compared with the baseline on average. In addition, Tetris Write earns 26%, 15% and 10% more read latency reduction, 15%, 7% and 5% more write latency reduction, and outperforms 22%, 12% and 7% more running time reduction, compared with the state-of-the-art Flip-N-Write, 2-Stage-Write and Three-Stage-Write schemes, whose IPC improvements are 1.4X, 1.6X and 1.8X, respectively.

Phase Change Memory (PCM) is one of the promising memory technologies but suffers from some critical problems such as poor write performance and high write energy consumption. Due to the high write energy consumption and limited power supply, the size of concurrent bit-write is restricted inside one PCM chip. Typically, the size of concurrent bit-write is much less than the cache line size and it is normal that many serially executed write units are consumed to write down the data block to PCM when using it as the main memory. Existing state-of-the-art PCM write schemes, such as FNW (Flip-N-Write) and two-stage-write, address the problem of poor performance by improving the write parallelism under the power constraints. The parallelism is obtained via reducing the data amount and leveraging power as well as time asymmetries, respectively. However, due to the extremely pessimistic assumptions of current utilization (FNW) and optimistic assumptions of asymmetries (two-stage-write), these schemes fail to maximize the power supply utilization and hence improve the write parallelism. In this article, we propose a novel PCM write scheme, called MaxPB (Maximize the Power Budget utilization) to maximize the power budget utilization with minimum changes about the circuits design. MaxPB is a “think before acting” method. The main idea of MaxPB is to monitor the actual power needs of all data units first and then effectively package them into the least number of write units under the power constraints. Experimental results show the efficiency and performance improvements on MaxPB. For example, four-core PARSEC and SPEC experimental results show that MaxPB gets 32.0% and 20.3% more read latency reduction, 26.5% and 16.1% more write latency reduction, 24.3% and 15.6% more running time decrease, 1.32× and 0.92× more speedup, as well as 30.6% and 18.4% more energy consumption reduction on average compared with the state-of-the-art FNW and two-stage-write write schemes, respectively.