Toward a Hardware Architecture for Belief-Desire-Intention-based Agents

面向基于信念-愿望-意图的智能体的硬件架构

• 批准号: 558263-2020
• 负责人: Esfandiari, Babak
• 金额: $ 1.46万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721676
• 关键词: Toward; Hardware; Architecture; Belief; Desire

With the current wide availability of robotic platforms and the operating systems to support them, it is now possible to implement their control using high-level plan monitoring and execution systems such as agent frameworks that rely on the Belief-Desire-Intention (BDI) paradigm. BDI allows the developer of an autonomous system to focus on implementing the various short-term plans (Intentions) and long-term goals (Desires) that the system can execute in the current context of its perceptions (Beliefs), and the BDI engine can then select, execute and monitor the appropriate plan given the current context. BDI has mostly only been evaluated in the context of simulated or toy environments, and so the performance aspects, especially when embedded in the real world, has not been investigated much to our knowledge. In particular, it is unknown what the actual bottlenecks in the execution of the BDI cycle are, and so what are the opportunities for hardware acceleration to address those bottlenecksToward this goal, we want to research the design of hardware architectures specifically suited for BDI systems. Whilst deep learning thrives on the super-pipelined, highly parallel fabric of GPUs, an ideal architecture for BDI has not been found. Our hypothesis is that this will require highly heterogeneous processing, interconnected with a memory hierarchy that reflects the data structures and access patterns representative of BDI implementations.We will set up a profiling system that allows us to instrument representative BDI implementations, and design strategies for automatically deriving hardware architectures that reflect the gathered profiles (through a combination of High Level Synthesis tools and custom hardware code generators). For each architecture, we will evaluate performance and power consumption for the set of BDI applications and assess bottlenecks.

随着机器人平台和操作系统的广泛可用性，现在可以使用高级计划监控和执行系统（如依赖于信念-愿望-意图（BDI）范式的代理框架）来实现其控制。BDI允许自治系统的开发人员专注于实现各种短期计划（意图）和长期目标（愿望），系统可以在其感知（信念）的当前上下文中执行这些计划，然后BDI引擎可以选择，执行和监视给定当前上下文的适当计划。BDI大多仅在模拟或玩具环境中进行评估，因此性能方面，特别是嵌入真实的世界时，据我们所知还没有进行过多的研究。特别是，它是未知的，在执行BDI周期的实际瓶颈是什么，所以什么是硬件加速的机会，以解决这些问题朝着这个目标，我们想研究的硬件架构的设计，特别适合BDI系统。虽然深度学习在GPU的超级流水线、高度并行结构上蓬勃发展，但还没有找到BDI的理想架构。我们的假设是，这将需要高度异构的处理，与反映BDI实现代表性的数据结构和访问模式的内存层次结构互连。我们将建立一个分析系统，使我们能够对代表性的BDI实现进行分析，以及用于自动导出反映所收集的简档的硬件架构的设计策略（通过高级合成工具和定制硬件代码生成器的组合）。对于每种架构，我们将评估BDI应用程序集的性能和功耗，并评估瓶颈。