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

在传统计算系统中，在存储器和处理单元之间传输数据在能量和等待时间方面是昂贵的。这种数据移动会消耗大量能量并降低处理速度，特别是对于机器学习工作负载等数据驱动型应用程序。内存计算(IMC)是通过在内存中执行计算来解决这一问题的一种很有前途的解决方案。然而，使用新兴存储技术的IMC技术受到多个关键技术挑战的限制，这些挑战限制了它们在当今计算系统中的适用性。这些IMC技术在计算中可能存在重大误差。处理等待时间也随着数据宽度的增加而显著增加；例如，对于乘法运算，这大约是指数增长。该项目通过利用一种简单而统一的数据表示来应对当今IMC技术的关键挑战。对加权二进制数据的复杂算术运算被转化为对均匀比特流的简单的逐位存储友好操作。该项目的成功完成将加速和降低从生物医学(例如视网膜植入物)到安全(例如微型无人机)到智能传感器和机器学习(例如语音识别)等广泛应用的电力和能源消耗。该团队将与研究社区分享项目成果，包括文章、模拟器以及硬件和软件工具包。调查结果和技术成果将被整合到研究生、本科生和K-12课堂环境的教学材料中。项目活动将吸引来自代表性不足群体的研究生和本科生的积极参与。该项目结合了IMC和一进制计算(UC)这两种新兴技术的互补特性，以实现一个高性能、可靠、高能效和高计算能力的数据处理平台。提出的平台解决了使用新兴存储器技术的IMC技术的技术挑战。它还解决了使用组合CMOS逻辑的现有UC设计的延迟和成本效益问题。本项目探索的技术旨在通过处理均匀的码流来提高IMC运算对噪声和变化的稳健性。该平台具有高度的并行性，并能有效地根据计算规模进行扩展。它能够完全在内存中快速、准确和简单地执行各种算术运算。它享有独立的逐位操作，避免了操作的长链，这是当今IMC技术效率低下的一个重要来源。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)计划以及既定的激励竞争研究计划(EPSCoR)联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.