New Paradigms for Edge Intelligence Hardware

随着第五代无线网络的到来，连接到互联网的设备爆炸式增长，导致每天产生的数据量呈指数级增长。这些数据通常被发送到遥远的仓库级云服务器，用深度神经网络等人工智能算法进行处理。现在有许多因素促成了边缘智能的兴起，这种处理发生在收集数据的地方或附近。我们的长期目标是开发技术，使普及的人工智能能够为本地用户和环境提供个性化的建议、决策和行动。我们将在短期内通过开发算法和硬件实现来实现这一目标，以实现低延迟、低功耗的设备上深度神经网络训练，以及边缘设备之间以及边缘设备与云之间的安全、超可靠的低延迟通信。我们的短期目标是：(1)为深度神经网络的设备上训练开发实用的随机计算方法；(2)开发无反向传播的设备上训练具有可逆逻辑的深度神经网络的方法；(3)实现短纠错码的超可靠、低延迟解码器。这项拟议工作的目的是实现低成本和节能智能设备的部署，为个性化医疗、智能家居、工业4.0、机器人和自动驾驶汽车等新兴应用提供普及的人工智能。由于拟议的工作处于算法和硬件的交叉点，它为培养高素质人才（HQP）提供了一个良好的环境，使他们获得开发先进边缘智能硬件的知识和技能，并继续为加拿大的工业和大学做出重要贡献。

The explosive growth of devices connected to the Internet, along with the arrival of the fifth generation wireless network, is leading to an exponential growth in the amount of data generated daily. Such data is typically sent to distant warehouse-scale cloud servers for processing with artificial intelligence (AI) algorithms such as deep neural networks. A number of factors are now contributing to the rise of edge intelligence, where such processing happens at or near where the data is collected. Our long-term goal is to develop technologies that enable pervasive AI to provide recommendations, decisions, and actions personalized to a local user and context. We will pursue this goal in the short-term by developing algorithms and hardware implementations for low-latency, low-power on-device training of deep neural networks and secure, ultra-reliable low-latency communications among edge devices and between edge devices and the cloud. Our short-term objectives are to (1) develop practical stochastic computing methods for on-device training of deep neural networks; (2) develop backpropagation-free on-device methods for training deep neural networks with invertible logic; and (3) implement ultra-reliable and low-latency decoders for short error-correcting codes. This aim of this proposed work is to enable the deployment of low-cost and energy-efficient intelligent devices providing pervasive AI to power emerging applications in personalized medicine, smart homes, Industry 4.0, robotics, and autonomous vehicles. As the proposed work lies at the intersection between algorithms and hardware, it provides an outstanding environment for training highly qualified personnel (HQP) to acquire knowledge and skills in developing advanced edge intelligence hardware and go on to make important contributions to Canadian industry and universities.