Jiangpeng Li

Bio: Jiangpeng Li is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Flash memory & MIMO. The author has an hindex of 7, co-authored 16 publications receiving 178 citations. Previous affiliations of Jiangpeng Li include Rensselaer Polytechnic Institute.

Reducing solid-state storage device write stress through opportunistic in-place delta compression

Abstract: Inside modern SSDs, a small portion of MLC/TLC NAND flash memory blocks operate in SLC-mode to serve as write buffer/cache and/or store hot data. These SLC-mode blocks absorb a large percentage of write operations. To balance memory wear-out, such MLC/TLC-to-SLC configuration rotates among all the memory blocks inside SSDs. This paper presents a simple yet effective design approach to reduce write stress on SLC-mode flash blocks and hence improve the overall SSD lifetime. The key is to implement well-known delta compression without being subject to the read latency and data management complexity penalties inherent to conventional practice. The underlying theme is to leverage the partial programmability of SLC-mode flash memory pages to ensure that the original data and all the subsequent deltas always reside in the same memory physical page. To avoid the storage capacity overhead, we further propose to combine intra-sector lossless data compression with intra-page delta compression, leading to opportunistic in-place delta compression. This paper presents specific techniques to address important issues for its practical implementation, including data error correction, and intra-page data placement and management. We carried out comprehensive experiments, simulations, and ASIC (application-specific integrated circuit) design. The results show that the proposed design solution can largely reduce the write stress on SLC-mode flash memory pages without significant latency overhead and meanwhile incurs relatively small silicon implementation cost.

How much can data compressibility help to improve NAND flash memory lifetime

Abstract: Although data compression can benefit flash memory lifetime, little work has been done to rigorously study the full potential of exploiting data compressibility to improve memory lifetime. This work attempts to fill this missing link. Motivated by the fact that memory cell damage strongly depends on the data content being stored, we first propose an implicit data compression approach (i.e., compress each data sector but do not increase the number of sectors per flash memory page) as a complement to conventional explicit data compression that aims to increase the number of sectors per flash memory page. Due to the runtime variation of data compressibility, each flash memory page almost always contains some unused storage space left by compressed data sectors. We develop a set of design strategies for exploiting such unused storage space to reduce the overall memory physical damage. We derive a set of mathematical formulations that can quantitatively estimate flash memory physical damage reduction gained by the proposed design strategies for both explicit and implicit data compression. Using 20nm MLC NAND flash memory chips, we carry out extensive experiments to quantify the content dependency of memory cell damage, based upon which we empirically evaluate and compare the effectiveness of the proposed design strategies under a wide spectrum of data compressibility characteristics.

Memory efficient layered decoder design with early termination for LDPC codes

Abstract: Layered structure is widely used in the design of Low-Density Parity-Check (LDPC) code decoders due to its fast convergence speed. However, correct checking process is difficult to implement in layered decoder, which results in unnecessary iterations. In this paper, an early termination strategy is presented for layered LDPC decoder to avoid redundant number of iterations. This approach makes use of the comparison between current log-likelyhood ratios (LLRs) and updated LLRs of all variable nodes to determine termination criteria of iterations. Furthermore, a non-uniform quantization scheme and an extrinsic messages memory optimization scheme are developed for memory savings. Based on these proposed methods, an LDPC decoder for the Chinese digital mobile TV applications is implemented using a SMIC 130nm CMOS process. The decoder consumes only 171 Kbits memory while achieving 267Mbps for code rate 1/2, and 401Mbps for code rate 3/4.

Realizing Unequal Error Correction for nand Flash Memory at Minimal Read Latency Overhead

Abstract: In nand Flash memory, all pages have the same storage capacity and hence accommodate the same amount of redundancy in support of error correction. In current practice, user data in all the pages are protected by the same error correction code. However, different types of pages in multibit per cell memory have largely different bit error rates, for which appropriate unequal error correction can achieve a better utilization of memory redundancy and hence improve program/erase (P/E) cycling endurance. Nevertheless, a straightforward realization of unequal error correction suffers from severe memory read latency penalty. This brief presents a design strategy to implement unequal error correction through concatenated coding, which can well match the unequal error rates among different types of pages at minimal memory read latency penalty. Based on measurement results from commercial sub-22-nm 2 bits/cell nand Flash memory chips, we carried out simulations from both the coding and storage system perspectives, and the results show that this design strategy can improve the P/E cycling endurance by 20% and only incur less than 7% increase of storage system read response time at the end of Flash memory lifetime with the P/E cycling of around 1800.

Realizing Transparent OS/Apps Compression in Mobile Devices at Zero Latency Overhead

Abstract: Motivated by the significant storage footprint of OS/Apps in mobile devices, this paper studies the realization of OS/Apps transparent compression. In spite of its obvious advantage, this feature however is not widely available in commercial mobile devices, which is due to the justifiable concern on the read latency penalty. In conventional practice on implementing transparent compression, read latency overhead comes from two aspects, including read amplification and decompression computational latency. This paper presents simple yet effective design solutions to eliminate the read amplification at the filesystem level and eliminate the computational latency overhead at the computer architecture level. To demonstrate its practical feasibility, we first implemented a prototyping filesystem to empirically verify the realization of transparent compression with zero read amplification. We further demonstrated that the OS/Apps footprint can be reduced by up to 39 percent on a Nexus 7 tablet installed with Android 5.0. Through application-specific integrated circuit (ASIC) synthesis, we show that the proposed computer architecture level design solution can eliminate the decompression latency overhead with very small silicon cost.

Google Workloads for Consumer Devices: Mitigating Data Movement Bottlenecks

Abstract: We are experiencing an explosive growth in the number of consumer devices, including smartphones, tablets, web-based computers such as Chromebooks, and wearable devices. For this class of devices, energy efficiency is a first-class concern due to the limited battery capacity and thermal power budget. We find that data movement is a major contributor to the total system energy and execution time in consumer devices. The energy and performance costs of moving data between the memory system and the compute units are significantly higher than the costs of computation. As a result, addressing data movement is crucial for consumer devices. In this work, we comprehensively analyze the energy and performance impact of data movement for several widely-used Google consumer workloads: (1) the Chrome web browser; (2) TensorFlow Mobile, Google's machine learning framework; (3) video playback, and (4) video capture, both of which are used in many video services such as YouTube and Google Hangouts. We find that processing-in-memory (PIM) can significantly reduce data movement for all of these workloads, by performing part of the computation close to memory. Each workload contains simple primitives and functions that contribute to a significant amount of the overall data movement. We investigate whether these primitives and functions are feasible to implement using PIM, given the limited area and power constraints of consumer devices. Our analysis shows that offloading these primitives to PIM logic, consisting of either simple cores or specialized accelerators, eliminates a large amount of data movement, and significantly reduces total system energy (by an average of 55.4% across the workloads) and execution time (by an average of 54.2%).

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

Abstract: 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.

A Survey of Software Techniques for Using Non-Volatile Memories for Storage and Main Memory Systems

Abstract: Non-volatile memory (NVM) devices, such as Flash, phase change RAM, spin transfer torque RAM, and resistive RAM, offer several advantages and challenges when compared to conventional memory technologies, such as DRAM and magnetic hard disk drives (HDDs). In this paper, we present a survey of software techniques that have been proposed to exploit the advantages and mitigate the disadvantages of NVMs when used for designing memory systems, and, in particular, secondary storage (e.g., solid state drive) and main memory. We classify these software techniques along several dimensions to highlight their similarities and differences. Given that NVMs are growing in popularity, we believe that this survey will motivate further research in the field of software technology for NVMs.

Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model

Abstract: Urban land-use change is affected by urban planning and government decision-making. Previous urban simulation methods focused only on planning constraints that prevent urban growth from developing ...