SHF: Small: Reliable Storage and Computation in Memory Technologies

SHF：小型：内存技术中的可靠存储和计算

• 批准号: 2113914
• 负责人: Nur Touba
• 金额: $ 48.06万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113914&HistoricalAwards=false
• 关键词: SHF; Small; Reliable; Storage; Computation

Memory circuits are a core building blocks for electronic systems and often are a bottleneck limiting system performance. A number of promising new memory technologies have emerged, including phase-chase memory, spin transfer torque memory, and resistive random access memory (RRAM), which offer significant improvements over conventional memory technologies in terms of speed, density, power consumption, and scalability. An exciting application for RRAM is its ability to perform matrix multiplication through current summation which significantly speeds up matrix-vector multiplication and thus making it ideal for implementing neural networks. A major challenge preventing widespread adoption of these emerging memory technologies is that they have a number of reliability issues that can cause errors during operation and shorten their useable lifetime. The goal of this project is to develop transformative approaches for improving the reliability of these emerging memory technologies to address these challenges. The educational impact of this project will include training of researchers in high impact areas, dissemination of new educational materials, and providing research opportunities for undergraduates and underrepresented groups.Error correcting codes (ECC) are widely using for conventional memories including in caches and main memory as well as secondary storage to protect against transient and permanent errors. However, direct application of the conventionally used ECC codes for emerging memory technologies in these applications is not feasible for several reasons, including significantly higher error rates, the need for faster decoding, and efficient application in multilevel memories. This project will investigate new ECC codes that can handle high error rates and are efficient for multilevel memories while providing high-speed decoding. RRAM is prone to a number of failure mechanisms that can result in either hard or soft errors that can affect the accuracy of matrix multiply computations. New methodologies to ensure reliable computation in RRAM will be investigated including both application-independent as well as application-dependent schemes that can use functional properties to reduce overheating.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

存储器电路是电子系统的核心构建块，并且通常是限制系统性能的瓶颈。 已经出现了许多有前途的新存储器技术，包括相位追踪存储器、自旋转移矩存储器和电阻式随机存取存储器（RRAM），其在速度、密度、功耗和可扩展性方面提供了相对于常规存储器技术的显著改进。 RRAM的一个令人兴奋的应用是它能够通过电流求和来执行矩阵乘法，这大大加快了矩阵向量乘法，因此使其成为实现神经网络的理想选择。阻止这些新兴存储器技术广泛采用的主要挑战是它们具有许多可靠性问题，这些问题可能在操作期间导致错误并缩短其可用寿命。 该项目的目标是开发变革性的方法，以提高这些新兴存储器技术的可靠性，以应对这些挑战。该项目的教育影响将包括培训高影响领域的研究人员，传播新的教育材料，并为本科生和代表性不足的群体提供研究机会。纠错码（ECC）广泛用于传统存储器，包括高速缓存和主存储器以及辅助存储器，以防止瞬时和永久错误。 然而，在这些应用中直接应用传统使用的ECC码用于新兴的存储器技术是不可行的，原因有几个，包括显著更高的错误率，需要更快的解码，以及在多级存储器中的有效应用。 本项目将研究新的ECC码，可以处理高错误率，并在提供高速解码的同时对多级存储器有效。 RRAM容易出现许多故障机制，这些故障机制可能导致硬错误或软错误，从而影响矩阵乘法计算的准确性。 新的方法，以确保可靠的计算在RRAM将调查，包括两个独立的应用程序，以及应用程序依赖的计划，可以使用功能属性，以减少过热。这一奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。