Autonomous NAnotech GRAph Memory (ANAGRAM)

自主纳米技术图形存储器 (ANAGRAM)

基本信息

批准号：
EP/V008242/1
负责人：
Alexantrou Serb
金额：
$ 44.25万
依托单位：
University of Southampton
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV008242%2F1
关键词：
Autonomous NAnotech GRAph Memory ANAGRAM

项目摘要

Artificial intelligence (AI) is transforming our societies, but the more it proliferates, the higher the customer demands for functionality and efficiency (most notably energy). Thus, as time progresses the limitations of statistical learning-based AI that has underpinned most AI work so far are beginning to naturally become more exposed. Tasks such as variable binding and manipulation, inductive reasoning and 1-shot learning, at which statistical learning is not as strong, suggest solutions in the sphere of abstract symbol processing AI. The commonly referenced 'next wave of AI' that is capable of such exploits (towards "strong AI") is likely to make extensive use of symbol processing capabilities and simultaneously demand a bespoke set of hardware solutions. The proposed project primarily addresses the issue of developing general-purpose (platform-level) hardware for precisely symbolic AI.The proposed project seeks to develop a memory module that features: a) an internal structure and b) in-memory computing capabilities that render it particularly suitable for symbolic processing-based artificial intelligence (AI) systems. Ultimately the project seeks to deliver: 1) Two microchip iterations prototyping the memory system. 2) A software environment (infrastructure) for easy programming and operation of the resulting microchips (includes simulation capabilities for proof-of-concept tests). 3) A demonstration of the memory cell operating together with a symbolic processor as an aggregate system. 4) A functioning set of starter applications illustrating the capabilities of the design.The overall effort is driven by a philosophy of co-optimising the memory across the entire trio of fundamental device components, symbolic AI mechanics and hardware design facets. Specifically: functionality in the proposed memory system will be pursued by: a) Designing a resistive RAM-based (ReRAM) memory unit where operation of the ReRAM devices and ReRAM tech specifications themselves are subservient to the specific operational goals of the memory system. b) Adapting the mathematical machinery of the system in order to map functional operations to hardware-friendly machine-code level operations: the stress is on hardware-friendliness, not mathematical elegance. This will be inextricably linked to the design of the memory's instruction set. c) Designing an architecture that runs the symbolic memory efficiently by using memory allocation techniques that maximise locality and making extensive use of power-gating. Simultaneously, implementation of a solid software stack infrastructure will enable efficient and fast prototyping and hypothesis testing.The cornerstone of the targeted project impact is to lay the foundations for launching an industrial-scale design effort towards hardware for symbolic AI. Hence the bulk of the effort is in chip design (prototype-based de-risking of the idea) and toolchain development (impact acceleration by lowering barriers to user uptake). Simultaneously, it is expected that the project will play a significant role in enhancing interest in symbol-level AI and very crucially, inducing interest in connecting symbolic AI with statistical learning one; thereby significant impact on knowledge is achieved. Finally, the increased in the capabilities of AI, as well as the transparency of decision-making (typically readily expressible via formal expressions or even in pseudo-natural language) offered by the symbolic approach promise to make a significant impact in enhancing acceptance of AI by society, providing a solid scientific foundation for certification processes (AI trust - broadening the scope of applications that accept an AI solution). With hardware available for this task, significant impact on productivity and quality of life is to be achieved.The project is self-contained and is designed to launch a much broader, sustainable effort, headed by the PI in this field.

人工智能（AI）正在改变我们的社会，但是它越生动，客户对功能和效率的要求越高（最著名的是能源）。因此，随着时间的流逝，基于统计学习的AI的局限性到目前为止，为大多数AI工作的基础，自然而然地变得更加暴露。诸如可变结合和操纵，归纳推理和1-Shot学习（统计学习不那么强大）之类的任务建议在抽象符号处理AI领域的解决方案。通常引用的“下一波AI”能够进行此类利用（朝向“强AI”）可能会广泛使用符号处理功能，同时要求一组定制的硬件解决方案。拟议的项目主要解决了为精确符号AI开发通用（平台级）硬件的问题。拟议的项目旨在开发一个具有特征的内存模块：a）内部结构和b）内存计算功能，使其中内置的计算功能使其特别适合基于符号处理的基于基于符号处理的人工智能（AI）系统。最终，该项目试图交付：1）两次微芯片迭代原型对内存系统进行了构想。 2）软件环境（基础架构），可轻松地编程和运行所得的微芯片（包括概念证明测试的模拟功能）。 3）与符号处理器一起作为聚合系统运行的存储单元的演示。 4）一组功能的入门应用程序说明了设计的功能。整体努力是由在整个基本设备组件，符号AI机械师和硬件设计方面合作记忆的理念所驱动的。具体来说：拟议的内存系统中的功能将由以下操作以下操作： b）调整系统的数学机制，以将功能操作映射到硬件友好的机器代码级操作：压力是在硬件友好性上，而不是数学优雅。这将与内存指令集的设计密不可分。 c）设计一种通过使用内存分配技术来有效地运行符号内存的体系结构，该技术可以最大程度地提高局部性并广泛使用电源。同时，实施坚实的软件堆栈基础架构将实现有效且快速的原型和假设测试。目标项目影响的基石是为对符号AI的硬件启动工业规模的设计工作奠定基础。因此，大部分努力是在芯片设计（基于原型的De危险中）和工具链开发（通过降低用户吸收的障碍来加速）。同时，预计该项目将在增强对符号级AI的兴趣方面发挥重要作用，并且非常关键，从而引起对将符号AI与统计学习联系起来的兴趣。从而实现了知识的重大影响。 Finally, the increased in the capabilities of AI, as well as the transparency of decision-making (typically readily expressible via formal expressions or even in pseudo-natural language) offered by the symbolic approach promise to make a significant impact in enhancing acceptance of AI by society, providing a solid scientific foundation for certification processes (AI trust - broadening the scope of applications that accept an AI solution).借助用于此任务的硬件，将对生产力和生活质量产生重大影响。该项目是独立的，旨在发起更广泛，更可持续的努力，由PI在该领域的领导。