Collaborative Research: SHF: Medium: A Comprehensive Modeling Framework for Cross-Layer Benchmarking of In-Memory Computing Fabrics: From Devices to Applications

该项目将开发一个框架，用于应用级工作负载的快速和准确的设计空间探索，假设技术支持内存计算（IMC），目前正在研究一系列应用空间（人工智能/机器学习，生物信息学，图形处理等）。随着越来越多的计算工作负载必须处理不断增长的数据量，IMC变得非常有趣。通常，与数据从计算机内存传输到处理器相关的能量和延迟可能超过处理本身的成本。因此，将处理和内存放在一起是非常可取的。该项目的工作将产生一个公开可用的、精心策划的框架，该框架利用现有的设备模型和设计工具，并结合新的设备模型和设计工具，以适当地评估大规模应用级工作负载的IMC设计空间。建模和评估基础设施将被开发来解决上述设计/评估挑战，因为显然需要探索广阔的设计空间。该项目的研究人员还将与K-8教师合作，增加现有STEM课程的材料，使学生了解计算机科学的基本概念和技能。这一点尤其重要，因为计算机科学概念现在是在全州标准化考试中评估的。来自代表性不足群体的学生将通过REU的经验进行招募和指导。为了探索IMC设计空间，必须研究设备级建模、电路/架构级建模、设备非理想性（例如，变化）分析，以及集成针对特定应用级工作负载的异构架构解决方案的方法。在IMC领域，(i)候选技术的数量庞大且不断变化，（ii）存在多个候选IMC电路和架构——例如，阵列外围计算（CAP）、内容可寻址存储器（CAMs）和交叉棒，（iii） IMC解决方案可能更容易受到设备变化/非理想性的影响，这种影响必须在应用层面加以捕捉。（iv）新兴技术支持的集成集成管理解决方案可以与现有的架构解决方案和/或各种异质设计一起使用，以及(v)人们可以考虑的应用级映射/潜在的算法更改实际上是无限多的。在器件模型方面，由于对该技术日益增长的兴趣以及考虑单片3D处理/存储系统的需要，人们有意关注铁电器件——即前端硅铁电场效应晶体管、后端金属氧化物铁电场效应晶体管和多栅极铁电场效应晶体管。对于IMC电路/架构，该项目将扩展和开发两种不同“风格”的内存计算建模/评估工具- (i) CAM（可以报告最匹配给定查询的内存条目）和（ii） CAP。对于CAM，代表性的工作包括预测二元，三元，多级和模拟CAM阵列（读/写能量和延迟等）设计的优点，这些设计采用不同的非易失性存储器，用于不同的匹配功能。确定最佳凸轮阵列尺寸和其他设计参数也将被考虑。对不同nvm的CAP设计进行评估也将展开。对于应用程序，将评估基于MLPerf应用程序子集的IMC结构的解决方案。MLPerf代表了一个由人工智能领导者组成的联盟，他们在视觉、语言等方面获得了相关的工作量。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。

This project will develop a framework for rapid and accurate design-space explorations of application-level workloads assuming technology-enabled in-memory computing (IMC), which is at present being investigated for a range of application spaces (AI/machine learning, bioinformatics, graph processing, etc.). IMC is of great interest as more and more compute workloads must process ever growing amounts of data. Frequently, the energy and latency associated with data transfer from a computer’s memory to a processor can overwhelm the cost of the processing itself. As such, it is highly desirable to co-locate processing and memory. Work in the project will result a publicly available, curated framework that leverages both existing device models and design tools, and that incorporates new device models and design tools to properly evaluate the IMC design space with at-scale, application-level workloads. A modeling and evaluation infrastructure will be developed to address the above design/evaluation challenges as there is an obvious need to explore a vast design space. Investigators in this project will also work with K-8 teachers to augment existing STEM curricula with material that exposes students to fundamental concepts and skills in computer science. This is especially relevant as computer science concepts are now assessed on state-wide standardized tests. Students from under-represented groups will be recruited and mentored via REU experiences.To explore the IMC design space, device-level modeling, circuit/architectural-level modeling, device non-ideality (e.g., variation) analysis, and ways to integrate heterogeneous architectural solutions that target specific application-level workloads must all be studied. In the IMC space, (i) the number of candidate technologies is large and ever-changing, (ii) multiple candidate IMC circuits and architectures – e.g., computing at the array periphery (CAP), content addressable memories (CAMs) and crossbars – exist, (iii) IMC solutions may be more susceptible to device variations/non-idealities, and this impact must be captured at the application level, (iv) emerging technology-enabled IMC solutions may be used with existing architectural solutions and/or in a variety of heterogenous designs, and (v) there are effectively an infinite number of application-level mappings/potential algorithmic changes that one might consider. With respect to device models, there is a deliberate focus on ferroelectric devices – i.e., front-end-of-line silicon ferroelectric field effect transistors, back-end-of-line metal-oxide ferroelectric field effect transistors, and multi-gate ferroelectric field effect transistors – owing to ever-growing interest in this technology as well as the need to consider monolithic 3D processing/memory systems. For IMC circuits/architectures, this project will expand and develop modeling/evaluation tools for two different “flavors” of computing in memory – (i) CAMs (that can report memory entries that best match a given query) and (ii) CAP. For CAMs, representative efforts include projecting figures of merit for binary, ternary, multi-level, and analog CAM arrays (read/write energy and latency, etc.) designs implemented with different non-volatile memories, for different matching functions. Determining optimal CAM array sizes and other design parameters will also be considered. Evaluation of CAP designs for different NVMs will also be developed. For applications, solutions based on IMC fabrics for a subset of applications from MLPerf will be evaluated. MLPerf represents a consortium of AI leaders who have derived relevant workloads for vision, language, etc.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.