CCF-BSF:CIF: Small: Coding for Fast Storage Access and In-Memory Computing

CCF-BSF:CIF：小型：快速存储访问和内存计算的编码

• 批准号: 1718389
• 负责人: Lara Dolecek
• 金额: $ 47万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718389&HistoricalAwards=false
• 关键词: CCF; BSF; CIF; Small; Coding

Part 1:Driven by the needs of mobile and cloud computing, demand for data storage is exhibiting steep growth, both in the direction of higher storage density as well as a simultaneous ambitious increase in access performance. A related exciting emerging trend driven by access challenges is in-memory computing, whereby computations are offloaded from the main processing units to the memory to reduce transfer time and energy. The challenges of future rapid storage access and in-memory computing cannot be addressed by the conventional storage architectures that inevitably trade off reliability and capacity for latency. This bottleneck calls for innovative research contributions that can simultaneously maximize the storage density, access performance, and computing functionality. This project addresses this imminent challenge by developing principled mathematical foundations that will underpin future computing systems possessing qualities necessary to address new data-intensive applications, focusing on fundamental performance bounds, algorithms, and practical channel coding methods. The results of this project will be demonstrated on modern data-driven and machine learning applications, will advance the repertoire of mathematical techniques in information sciences, and will directly impact future computer system architectures to meet the growing and wide ranging societal and scientific needs for computing and rapid data processing. Additionally, the proposal offers several mechanisms for broader impacts, including engagement with data storage and memory industry through the existing research center that the principal investigator is leading at UCLA, curriculum development and the introduction of new graduate courses in the UCLA on-line master's program in engineering, engagement of undergraduate researchers, and dissemination of the results through survey-style articles and tutorials.Part 2: The project has the following three complementary research goals:1) Invention of new channel codes for reliable and fast memory access for latency sensitive applications, with the study spanning general memories and specific schemes for resistive memories in particular. The proposed schemes will offer non-trivial extensions to vibrant coding subjects: codes with locality (algebraic and graph-based) and constrained coding; 2) Invention of new channel codes for which the decoding is performed directly in memory to enable simultaneously satisfying competing requirements on latency and reliability. Here, the decoder itself is subject to computational errors, themselves manifested in a data dependent sense. The analysis will lead to bounds and practical code designs robust to data-dependent errors. An exemplar will be codes designed using spatial coupling and decoded using windowed message passing decoders;3) Development of novel fundamental bounds, algorithms, and channel codes for robust in-memory computing, with the focus on quantifying the robustness of computing primitives in statistical inference and other machine learning algorithms used in modern data-driven applications. These include fundamental performance limits and new coding-based methods to simultaneously combat sneak paths and computing noise. Analysis will include coding for (noisy) Hamming/Euclidean similarity calculations, evaluated in the context of practical machine learning applications.Results from this project will also contribute to the curriculum development at UCLA and will offer new opportunities for the engagement of undergraduate researchers from underrepresented groups.

第1部分：在移动和云计算的需求驱动下，对数据存储的需求呈现出陡峭的增长，既朝着更高的存储密度以及同时雄心勃勃的访问性能提高。由访问挑战驱动的相关令人兴奋的新兴趋势是内存计算，从而将计算从主要处理单元转移到内存以减少传输时间和能量。传统的存储体系结构不可避免地要交易可靠性和延迟能力，无法解决未来快速存储访问和内存计算的挑战。这种瓶颈需要创新的研究贡献，可以同时提高存储密度，访问性能和计算功能。该项目通过开发有原则的数学基础来解决这一迫在眉睫的挑战，这些基础将支持未来的计算系统，该计算系统具有解决新的数据密集型应用程序所需的质量，重点关注基本性能范围，算法和实用的渠道编码方法。该项目的结果将在现代数据驱动和机器学习应用程序上展示，将推进信息科学中数学技术的曲目，并将直接影响未来的计算机系统体系结构，以满足不断增长且范围广泛的社会和科学需求，以实现计算和快速数据处理。 Additionally, the proposal offers several mechanisms for broader impacts, including engagement with data storage and memory industry through the existing research center that the principal investigator is leading at UCLA, curriculum development and the introduction of new graduate courses in the UCLA on-line master's program in engineering, engagement of undergraduate researchers, and dissemination of the results through survey-style articles and tutorials.Part 2: The project has the following three complementary research目标：1）针对延迟敏感应用的可靠和快速内存访问的新通道代码的发明，研究涵盖了一般记忆和特定方案，尤其是电阻性记忆的特定方案。所提出的方案将为充满活力的编码主体提供非平凡的扩展：具有局部性的代码（代数和基于图形）和约束编码； 2）在内存中直接执行解码的新频道代码的发明，以同时满足对延迟和可靠性的竞争要求。在这里，解码器本身受到计算错误的约束，本身以数据依赖性意义表现出来。该分析将导致界限和实用代码设计与数据相关的错误强大。示例将是使用空间耦合设计的代码，并使用窗户消息传递解码器进行解码； 3）开发新颖的基本界限，算法和频道代码，以实现强大的内存计算计算，重点是量化计算原则在统计范围学习和其他机器学习Algoriths中使用现代数据的可靠性。 这些包括基本的性能限制和新的基于编码的方法，以同时打击偷偷摸摸的路径和计算噪声。分析将包括在实用机器学习应用程序中评估的（嘈杂）锤子/欧几里得相似性计算。该项目的分解还将为UCLA的课程开发做出贡献，并将为少于代表性不足的小组提供不足的研究人员提供新的机会。

项目成果

A Channel-Aware Combinatorial Approach to Design High Performance Spatially-Coupled Codes

• DOI: 10.1109/tit.2020.2979981
• 发表时间: 2018-04
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Ahmed Hareedy;R. Wu;L. Dolecek

Hamming Distance Computation in Unreliable Resistive Memory

不可靠电阻存储器中的汉明距离计算

• DOI: 10.1109/tcomm.2018.2840717
• 发表时间: 2018
• 期刊: IEEE Transactions on Communications
• 影响因子: 8.3
• 作者: Chen, Zehui;Schoeny, Clayton;Dolecek, Lara
• 通讯作者: Dolecek, Lara

A coding scheme for reliable in-memory hamming distance computation

• DOI: 10.1109/acssc.2017.8335653
• 发表时间: 2017-10
• 期刊: 2017 51st Asilomar Conference on Signals, Systems, and Computers
• 作者: Zehui Chen;Clayton Schoeny;Yuval Cassuto;L. Dolecek

Channel Coding for Nonvolatile Memory Technologies: Theoretical Advances and Practical Considerations

• DOI: 10.1109/jproc.2017.2694613
• 发表时间: 2017-05
• 期刊: Proceedings of the IEEE
• 影响因子: 20.6
• 作者: L. Dolecek;Yuval Cassuto

Error Correction and Detection for Computing Memories Using System Side Information

使用系统侧信息对计算存储器进行纠错和检测

• DOI: 10.1109/itw.2018.8613473
• 发表时间: 2018
• 期刊: IEEE Information Theory Workshop
• 作者: Schoeny, Clayton;Alam, Irina;Gottscho, Mark;Gupta, Puneet;Dolecek, Lara
• 通讯作者: Dolecek, Lara