Saugata Ghose

Read Disturb Errors in MLC NAND Flash Memory: Characterization, Mitigation, and Recovery

NAND flash memory reliability continues to degrade as the memory is scaled down and more bits are programmed per cell. A key contributor to this reduced reliability is read disturb, where a read to one row of cells impacts the threshold voltages of unread flash cells in different rows of the same block. Such disturbances may shift the threshold voltages of these unread cells to different logical states than originally programmed, leading to read errors that hurt endurance. For the first time in open literature, this paper experimentally characterizes read disturb errors on state-of-the-art 2Y-nm (i.e., 20-24 nm) MLC NAND flash memory chips. Our findings (1) correlate the magnitude of threshold voltage shifts with read operation counts, (2) demonstrate how program/erase cycle count and retention age affect the read-disturb-induced error rate, and (3) identify that lowering pass-through voltage levels reduces the impact of read disturb and extend flash lifetime. Particularly, we find that the probability of read disturb errors increases with both higher wear-out and higher pass-through voltage levels. We leverage these findings to develop two new techniques. The first technique mitigates read disturb errors by dynamically tuning the pass-through voltage on a per-block basis. Using real workload traces, our evaluations show that this technique increases flash memory endurance by an average of 21%. The second technique recovers from previously-uncorrectable flash errors by identifying and probabilistically correcting cells susceptible to read disturb errors. Our evaluations show that this recovery technique reduces the raw bit error rate by 36%.

Low-Cost Inter-Linked Subarrays (LISA): Enabling fast inter-subarray data movement in DRAM

This paper introduces a new DRAM design that enables fast and energy-efficient bulk data movement across subarrays in a DRAM chip. While bulk data movement is a key operation in many applications and operating systems, contemporary systems perform this movement inefficiently, by transferring data from DRAM to the processor, and then back to DRAM, across a narrow off-chip channel. The use of this narrow channel for bulk data movement results in high latency and energy consumption. Prior work proposed to avoid these high costs by exploiting the existing wide internal DRAM bandwidth for bulk data movement, but the limited connectivity of wires within DRAM allows fast data movement within only a single DRAM subarray. Each subarray is only a few megabytes in size, greatly restricting the range over which fast bulk data movement can happen within DRAM. We propose a new DRAM substrate, Low-Cost Inter-Linked Subarrays (LISA), whose goal is to enable fast and efficient data movement across a large range of memory at low cost. LISA adds low-cost connections between adjacent subarrays. By using these connections to interconnect the existing internal wires (bitlines) of adjacent subarrays, LISA enables wide-bandwidth data transfer across multiple subarrays with little (only 0.8%) DRAM area overhead. As a DRAM substrate, LISA is versatile, enabling an array of new applications. We describe and evaluate three such applications in detail: (1) fast inter-subarray bulk data copy, (2) in-DRAM caching using a DRAM architecture whose rows have heterogeneous access latencies, and (3) accelerated bitline precharging by linking multiple precharge units together. Our extensive evaluations show that each of LISAs three applications significantly improves performance and memory energy efficiency, and their combined benefit is higher than the benefit of each alone, on a variety of workloads and system configurations.

Improving memory scheduling via processor-side load criticality information

We hypothesize that performing processor-side analysis of load instructions, and providing this pre-digested information to memory schedulers judiciously, can increase the sophistication of memory decisions while maintaining a lean memory controller that can take scheduling actions quickly. This is increasingly important as DRAM frequencies continue to increase relative to processor speed. In this paper we propose one such mechanism, pairing up a processor-side load criticality predictor with a lean memory controller that prioritizes load requests based on ranking information supplied from the processor side. Using a sophisticated multi-core simulator that includes a detailed quad-channel DDR3 DRAM model, we demonstrate that this mechanism can improve performance significantly on a CMP, with minimal overhead and virtually no changes to the processor itself. We show that our design compares favorably to several state-of-the-art schedulers.

Simultaneous Multi-Layer Access: Improving 3D-Stacked Memory Bandwidth at Low Cost

3D-stacked DRAM alleviates the limited memory bandwidth bottleneck that exists in modern systems by leveraging through silicon vias (TSVs) to deliver higher external memory channel bandwidth. Today’s systems, however, cannot fully utilize the higher bandwidth offered by TSVs, due to the limited internal bandwidth within each layer of the 3D-stacked DRAM. We identify that the bottleneck to enabling higher bandwidth in 3D-stacked DRAM is now the global bitline interface, the connection between the DRAM row buffer and the peripheral IO circuits. The global bitline interface consists of a limited and expensive set of wires and structures, called global bitlines and global sense amplifiers, whose high cost makes it difficult to simply scale up the bandwidth of the interface within a single DRAM layer in the 3D stack. We alleviate this bandwidth bottleneck by exploiting the observation that several global bitline interfaces already exist across the multiple DRAM layers in current 3D-stacked designs, but only a fraction of them are enabled at the same time. We propose a new 3D-stacked DRAM architecture, called Simultaneous Multi-Layer Access (SMLA), which increases the internal DRAM bandwidth by accessing multiple DRAM layers concurrently, thus making much greater use of the bandwidth that the TSVs offer. To avoid channel contention, the DRAM layers must coordinate with each other when simultaneously transferring data. We propose two approaches to coordination, both of which deliver four times the bandwidth for a four-layer DRAM, over a baseline that accesses only one layer at a time. Our first approach, Dedicated-IO, statically partitions the TSVs by assigning each layer to a dedicated set of TSVs that operate at a higher frequency. Unfortunately, Dedicated-IO requires a nonuniform design for each layer (increasing manufacturing costs), and its DRAM energy consumption scales linearly with the number of layers. Our second approach, Cascaded-IO, solves both issues by instead time multiplexing all of the TSVs across layers. Cascaded-IO reduces DRAM energy consumption by lowering the operating frequency of higher layers. Our evaluations show that SMLA provides significant performance improvement and energy reduction across a variety of workloads (55%/18% on average for multiprogrammed workloads, respectively) over a baseline 3D-stacked DRAM, with low overhead.

WARM: Improving NAND flash memory lifetime with write-hotness aware retention management

Increased NAND flash memory density has come at the cost of lifetime reductions. Flash lifetime can be extended by relaxing internal data retention time, the duration for which a flash cell correctly holds data. Such relaxation cannot be exposed externally to avoid altering the expected data integrity property of a flash device. Reliability mechanisms, most prominently refresh, restore the duration of data integrity, but greatly reduce the lifetime improvements from retention time relaxation by performing a large number of write operations. We find that retention time relaxation can be achieved more efficiently by exploiting heterogeneity in write-hotness, i.e., the frequency at which each page is written. We propose WARM, a write-hotness aware retention management policy for flash memory, which identifies and physically groups together write-hot data within the flash device, allowing the flash controller to selectively perform retention time relaxation with little cost. When applied alone, WARM improves overall flash lifetime by an average of 3.24× over a conventional management policy without refresh, across a variety of real I/O workload traces. When WARM is applied together with an adaptive refresh mechanism, the average lifetime improves by 12.9×, 1.21× over adaptive refresh alone.

Exploiting Inter-Warp Heterogeneity to Improve GPGPU Performance

In a GPU, all threads within a warp execute the same instruction in lockstep. For a memory instruction, this can lead to memory divergence: the memory requests for some threads are serviced early, while the remaining requests incur long latencies. This divergence stalls the warp, as it cannot execute the next instruction until all requests from the current instruction complete. In this work, we make three new observations. First, GPGPU warps exhibit heterogeneous memory divergence behavior at the shared cache: some warps have most of their requests hit in the cache (high cache utility), while other warps see most of their request miss (low cache utility). Second, a warp retains the same divergence behavior for long periods of execution. Third, due to high memory level parallelism, requests going to the shared cache can incur queuing delays as large as hundreds of cycles, exacerbating the effects of memory divergence. We propose a set of techniques, collectively called Memory Divergence Correction (MeDiC), that reduce the negative performance impact of memory divergence and cache queuing. MeDiC uses warp divergence characterization to guide three components: (1) a cache bypassing mechanism that exploits the latency tolerance of low cache utility warps to both alleviate queuing delay and increase the hit rate for high cache utility warps, (2) a cache insertion policy that prevents data from highcache utility warps from being prematurely evicted, and (3) a memory controller that prioritizes the few requests received from high cache utility warps to minimize stall time. We compare MeDiC to four cache management techniques, and find that it delivers an average speedup of 21.8%, and 20.1% higher energy efficiency, over a state-of-the-art GPU cache management mechanism across 15 different GPGPU applications.

LazyPIM: An Efficient Cache Coherence Mechanism for Processing-in-Memory

Processing-in-memory (PIM) architectures cannot use traditional approaches to cache coherence due to the high off-chip traffic consumed by coherence messages. We propose LazyPIM, a new hardware cache coherence mechanism designed specifically for PIM. LazyPIM uses a combination of speculative cache coherence and compressed coherence signatures to greatly reduce the overhead of keeping PIM coherent with the processor. We find that LazyPIM improves average performance across a range of PIM applications by 49.1 percent over the best prior approach, coming within 5.5 percent of an ideal PIM mechanism.

Error Characterization, Mitigation, and Recovery in Flash-Memory-Based Solid-State Drives

NAND flash memory is ubiquitous in everyday life today because its capacity has continuously increased and cost has continuously decreased over decades. This positive growth is a result of two key trends: 1) effective process technology scaling; and 2) multi-level (e.g., MLC, TLC) cell data coding. Unfortunately, the reliability of raw data stored in flash memory has also continued to become more difficult to ensure, because these two trends lead to 1) fewer electrons in the flash memory cell floating gate to represent the data; and 2) larger cell-to-cell interference and disturbance effects. Without mitigation, worsening reliability can reduce the lifetime of NAND flash memory. As a result, flash memory controllers in solid-state drives (SSDs) have become much more sophisticated: they incorporate many effective techniques to ensure the correct interpretation of noisy data stored in flash memory cells. In this article, we review recent advances in SSD error characterization, mitigation, and data recovery techniques for reliability and lifetime improvement. We provide rigorous experimental data from state-of-the-art MLC and TLC NAND flash devices on various types of flash memory errors, to motivate the need for such techniques. Based on the understanding developed by the experimental characterization, we describe several mitigation and recovery techniques, including 1) cell-to-cell interference mitigation; 2) optimal multi-level cell sensing; 3) error correction using state-of-the-art algorithms and methods; and 4) data recovery when error correction fails. We quantify the reliability improvement provided by each of these techniques. Looking forward, we briefly discuss how flash memory and these techniques could evolve into the future.

Understanding Reduced-Voltage Operation in Modern DRAM Devices: Experimental Characterization, Analysis, and Mechanisms

The energy consumption of DRAM is a critical concern in modern computing systems. Improvements in manufacturing process technology have allowed DRAM vendors to lower the DRAM supply voltage conservatively, which reduces some of the DRAM energy consumption. We would like to reduce the DRAM supply voltage more aggressively, to further reduce energy. Aggressive supply voltage reduction requires a thorough understanding of the effect voltage scaling has on DRAM access latency and DRAM reliability. In this paper, we take a comprehensive approach to understanding and exploiting the latency and reliability characteristics of modern DRAM when the supply voltage is lowered below the nominal voltage level specified by manufacturers.

Architectural support for low overhead detection of memory violations

Violations in memory references cause tremendous loss of productivity, catastrophic mission failures, loss of privacy and security, and much more. Software mechanisms to detect memory violations have high false positive and negative rates or huge performance overhead. This paper proposes architectural support to detect memory reference violations in inherently unsafe languages such as C and C++. In this approach, the ISA is extended to include ldquosafetyrdquo instructions that provide compile-time information on pointers and objects. The microarchitecture is extended to efficiently execute the safety instructions. We explore optimizations, such as delayed violation detection and stack-based handling of local pointers, to reduce the performance overhead. Our experiments show that the synergy between hardware and software results in this approach having less than 5% average performance overhead, while an exclusively software mechanism incurs 480% impact for the same benchmarks.