Autonomous NAnotech GRAph Memory (ANAGRAM)

人工智能（AI）正在改变我们的社会，但它的普及程度越高，客户对功能和效率（尤其是能源）的要求就越高。因此，随着时间的推移，迄今为止支撑大多数人工智能工作的基于统计学习的人工智能的局限性开始自然地暴露出来。变量绑定和操作、归纳推理和1次学习等任务，在统计学习不那么强大的情况下，在抽象符号处理AI领域提供了解决方案。通常提到的“下一波人工智能”能够利用这种漏洞（向“强人工智能”发展），可能会广泛使用符号处理能力，同时需要一套定制的硬件解决方案。提议的项目主要解决为精确符号人工智能开发通用（平台级）硬件的问题。该项目旨在开发一种内存模块，其特点是：a)内部结构和b)内存计算能力，使其特别适合基于符号处理的人工智能（AI）系统。最终，该项目寻求交付：1)两个微芯片迭代原型存储系统。2)软件环境（基础设施），便于编程和操作产生的微芯片（包括用于概念验证测试的模拟功能）。存储器单元与符号处理器作为一个聚合系统一起工作的演示。4)说明设计能力的启动器应用程序的功能集。整体努力是由共同优化内存的理念驱动的，包括基本设备组件、符号AI机制和硬件设计方面。具体而言：所提议的存储系统的功能将通过以下方式实现：a)设计一个基于电阻性ram （ReRAM）的存储单元，其中ReRAM设备的操作和ReRAM技术规范本身服从于存储系统的特定操作目标。b)调整系统的数学机制，以便将功能操作映射到硬件友好的机器代码级操作：重点是硬件友好，而不是数学优雅。这将与存储器指令集的设计紧密地联系在一起。c)通过使用最大化局部性和广泛使用功率门控的内存分配技术，设计一个有效运行符号内存的架构。同时，一个坚实的软件堆栈基础结构的实现将使高效和快速的原型和假设测试成为可能。目标项目影响的基石是为启动符号人工智能硬件的工业规模设计工作奠定基础。因此，大部分工作集中在芯片设计（基于原型的想法风险降低）和工具链开发（通过降低用户接受的障碍来加速影响）上。同时，预计该项目将在增强对符号级人工智能的兴趣方面发挥重要作用，并且非常重要的是，将符号人工智能与统计学习人工智能联系起来的兴趣；从而对知识产生重大影响。最后，人工智能能力的提高，以及象征性方法提供的决策透明度（通常易于通过正式表达式甚至伪自然语言表达），有望在提高社会对人工智能的接受程度方面产生重大影响，为认证过程提供坚实的科学基础（人工智能信任——扩大接受人工智能解决方案的应用范围）。有了可用于此任务的硬件，就可以实现对生产力和生活质量的重大影响。该项目是独立的，旨在发起一个更广泛的、可持续的努力，由PI在该领域领导。

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.