Developing Bio-inspired Neuro-Engine for Intelligent Pervasive Computing

开发用于智能普适计算的仿生神经引擎

• 批准号: RGPIN-2020-04869
• 负责人: Ahmadi, Arash
• 金额: $ 2.11万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752017
• 关键词: Developing; Bio; inspired; Neuro; Engine

Recent progress in computer science and engineering has introduced new computing paradigms such as the Internet of Things (IoT) and Ambient Intelligence (AmI). The core idea in these emerging technologies relies on distributed physical interaction with environments, in which low-cost computational nodes are ubiquitously connected within an infrastructure to collect and analyze data locally in real-time and favorable in terms of power consumption, communication overhead, security, and privacy. In this context, utilizing Artificial Intelligence (AI) and Machine Learning (ML) algorithms, which mostly come with significant computational complexity, would be highly challenging and requires immense improvements in computational efficiency at both the algorithmic and hardware level. Deep learning (DL), as a subset of ML, is in the heart of various artificial intelligence applications. Current DL applications are based on Artificial Neural Networks (ANN) consisting of neurons with continuous activation functions and a set of continuous-time weighted inputs. Although these networks are extremely powerful, they are also very resource hungry, which makes it very challenging to deploy them on edge computing nodes. Unlike ANNs, Spiking Neural Networks (SNN), use "asynchronous" and discrete-time spiking "events" to code, compute and transmit information. Consistent with biological behavior of Central Nervous System (CNS), these individual spikes are sparse in time and have a uniform amplitude, thus have the capability to carry information content by encoding data in the spike rate and/or timing. Due to the nature of spiking data coding and processing, SNNs are capable to be more hardware friendly and energy-efficient than ANNs and are thus very appealing for resource-restricted processors. Despite their rich mathematical and biological background, implementation and training deep SNNs remains a major challenge. Recently, a few promising methods have been developed to implement low-cost binarized ANN and spike-like outputs as well as training deep SNNs, which offer acceptable accuracy compared with lower hardware implementation cost. This research explores a tightly interwoven Algorithm-Hardware codesign techniques for low-cost spiking neuro-engines and follows implementation driven algorithmic innovations, together with customized yet flexible processing architectures, that can be utilized in ambient intelligent applications. The main goal of the research is based on the design and implementation of application-specific neuro-engines for embedded systems as a cross-disciplinary work on machine learning, bio-inspired computing, pervasive computing, and hardware design and optimization. Working with SpiNNaker group [1], as a postdoc fellow researcher, and several years of academic research in the field of bio-inspired computing has been a great source of inspiration to the applicant to conduct his research.

计算机科学和工程学的最新进展引入了新的计算范式，例如物联网（IoT）和环境智能（AMI）。这些新兴技术中的核心思想依赖于与环境的分布式物理互动，在这种情况下，低成本计算节点在基础架构中无处不在连接，以实时收集和分析数据，并在功耗，通信架空间接开销，安全性，安全性和隐私方面实时收集和有利。在这种情况下，利用人工智能（AI）和机器学习（ML）算法（主要是计算复杂性）将是高度挑战性的，并且需要在算法和硬件级别上的计算效率大大提高。 深度学习（DL）作为ML的一个子集，是各种人工智能应用的核心。当前的DL应用基于人工神经网络（ANN），该神经网络由具有连续激活函数和一组连续的加权输入组成。尽管这些网络非常强大，但它们也非常渴望资源，这使得它们在边缘计算节点上部署非常具有挑战性。与ANN不同，尖峰神经网络（SNN），使用“异步”和离散的时间峰值“事件”来代码，计算和传输信息。与中枢神经系统（CNS）的生物学行为一致，这些单个尖峰的时间很少，并且具有均匀的振幅，因此具有通过编码峰值速率和/或时间编码数据来携带信息内容的能力。 由于峰值数据编码和处理的性质，SNN能够比ANN更具硬件和节能性能，因此对资源限制的处理器非常有吸引力。尽管具有丰富的数学和生物学背景，但实施和训练深SNN仍然是一个主要挑战。最近，已经开发了一些有前途的方法来实施低成本的二元ANN和类似尖峰的输出以及训练深SNN，这些方法与较低的硬件实施成本相比提供了可接受的精度。 这项研究探讨了针对低成本尖峰神经发动机的紧密交织算法 - 硬件代码技术，并遵循实施驱动的算法创新，以及定制但灵活的处理体系结构，可用于环境智能应用程序。该研究的主要目的是基于用于嵌入式系统的应用特定神经引擎的设计和实施，作为机器学习，生物启发的计算，普遍计算以及硬件设计和优化的跨学科工作。与Spinnaker Group [1]合作，作为博士后研究员，并在生物启发的计算领域进行了数年的学术研究，这是申请人进行研究的重要灵感来源。