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: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716932
• 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.

计算机科学和工程的最新进展引入了新的计算范式，如物联网（IoT）和环境智能（AmI）。这些新兴技术的核心思想依赖于与环境的分布式物理交互，其中低成本的计算节点在基础设施中无处不在地连接，以实时收集和分析本地数据，并且在功耗、通信开销、安全性和隐私方面都很有利。在这种情况下，利用人工智能（AI）和机器学习（ML）算法，这些算法大多具有显著的计算复杂性，将是极具挑战性的，并且需要在算法和硬件层面上大幅提高计算效率。