New Paradigms for Edge Intelligence Hardware

边缘智能硬件的新范式

• 批准号: RGPIN-2021-02660
• 负责人: Gross, Warren
• 金额: $ 4.66万
• 依托单位: McGill 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=752111
• 关键词: New; Paradigms; Edge; Intelligence; Hardware

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.

连接到互联网的设备的爆炸式增长，沿着第五代无线网络的到来，导致每天生成的数据量呈指数级增长。这些数据通常被发送到远程仓库规模的云服务器，以便使用人工智能（AI）算法（如深度神经网络）进行处理。许多因素正在促进边缘智能的兴起，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，使无处不在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，以实现深度神经网络的低延迟、低功耗设备上训练，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）开发用于深度神经网络的设备上训练的实用随机计算方法;（2）开发用于训练具有可逆逻辑的深度神经网络的无反向传播设备上方法;（3）实现用于短纠错码的超可靠和低延迟解码器。这项拟议工作的目标是部署低成本和高能效的智能设备，提供普遍的人工智能，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动力。由于拟议的工作位于算法和硬件之间的交叉点，它为培训高素质人员（HQP）提供了一个出色的环境，以获得开发先进边缘智能硬件的知识和技能，并继续为加拿大工业和大学做出重要贡献。