CAREER: Unary Computing in Memory for Fast, Robust and Energy-Efficient Processing

职业：内存中的一元计算，实现快速、稳健和节能的处理

• 批准号: 2339701
• 负责人: M Hassan Najafi
• 金额: $ 60万
• 依托单位: University of Louisiana at Lafayette
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339701&HistoricalAwards=false
• 关键词: CAREER; Unary; Computing; Memory; Fast

Transferring data between memory and processing units in conventional computing systems is expensive in terms of energy and latency. This data movement consumes significant energy and slows down the processing speed, particularly for data-driven applications such as machine learning workloads. In-memory computing (IMC) is a promising solution to address this issue by performing computations inside memory. However, IMC techniques using emerging memory technologies suffer from multiple key technical challenges that limit their applicability in today’s computing systems. Significant error in the computations is likely with these IMC techniques. The processing latency also increases significantly with data-width; e.g., this is approximately an exponential increase for the multiplication operation. This project addresses the critical challenges with today’s IMC techniques by exploiting a simple and uniform representation of data. Complex arithmetic operations on weighted binary data are transformed into simple bit-wise memory-friendly operations on uniform bit-streams. Successful completion of the project will accelerate and reduce the power and energy consumption of a wide range of applications from biomedicine (e.g., retinal implants), to security (e.g., miniaturized unmanned aerial vehicles), to smart sensors and machine learning (e.g., speech recognition). The team will share the project outcomes, including articles, simulators, and hardware and software toolkits, with the research community. The findings and technical outputs will be integrated into instructional materials for graduate, undergraduate, and K-12 classroom settings. The project activities will engage active participation of graduate and undergraduate students from underrepresented groups.This project combines the complementary properties of two emerging technologies, IMC and unary computing (UC), to implement a high-performance, reliable, and energy-efficient data processing platform with high computational ability. The presented platform addresses the technical challenges of the IMC techniques using emerging memory technologies. It also addresses the latency and cost-efficiency of the existing UC designs with combinational CMOS logic. The technology explored in this project aims to improve the robustness of IMC operations to noise and variation by processing uniform bit-streams. The platform is highly parallel and effectively scales with the size of computations. It enables fast, accurate, and simple execution of a wide range of arithmetic operations entirely in memory. It enjoys independent bit-wise operations, avoiding long chains of operations, an important source of in-efficiency in today’s IMC techniques. The target platform is general and can be used for various applications.This project is jointly funded by the Software and Hardware Foundations (SHF) program in the Computing and Communication Foundations (CCF) division, and the Established Program to Stimulate Competitive Research (EPSCoR).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.

在传统计算系统中的内存和处理单元之间传输数据在能源和延迟方面是昂贵的，这种数据移动会消耗大量能源并降低处理速度，特别是对于机器学习工作负载等数据驱动的应用程序而言。然而，使用新兴内存技术的 IMC 技术面临着多个关键技术挑战，限制了它们在当今计算系统中的适用性。 IMC 技术。处理延迟也会随着数据宽度的增加而增加；例如，该项目通过利用简单且统一的数据表示来解决乘法运算的关键挑战。加权二进制数据被转换为统一比特流上的简单逐位内存友好操作，该项目的成功完成将加速并减少生物医学领域广泛应用的功耗和能源消耗。该团队将分享项目成果，包括文章、模拟器以及硬件和软件工具包。研究成果和技术成果将被纳入研究生、本科生和 K-12 课堂环境的教学材料中。该项目活动将吸引研究生和本科生的积极参与。该项目结合了 IMC 和一元计算 (UC) 两种新兴技术的互补特性，以实现具有高计算能力的高性能、可靠且节能的数据处理平台。所提出的平台解决了技术挑战。它还利用组合 CMOS 逻辑解决了现有 UC 设计的延迟和成本效率问题。该项目探索的技术旨在通过处理统一位来提高 IMC 操作对噪声和变化的鲁棒性。 - 平台。它是高度并行的，并且可以根据计算的大小进行有效扩展，它可以完全在内存中快速、准确和简单地执行各种算术运算，从而避免了长链运算，这是计算量的重要来源。当今的 IMC 技术效率低下。目标平台是通用的，可用于各种应用。该项目由计算和通信基础 (CCF) 部门的软件和硬件基础 (SHF) 计划以及制定刺激竞争性研究计划 (EPSCoR)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。