Research on the application of a new generation memory architecture in computer vision AI solutions for IoT devices

新一代内存架构在物联网设备计算机视觉AI解决方案中的应用研究

• 批准号: 10030177
• 金额: $ 43.68万
• 依托单位: BLUESHIFT MEMORY LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10030177
• 关键词: Research; application; new; generation; memory

Blueshift Memory is seeking to drive the development of a next generation of IoT Computer Vision (CV) application on edge devices. Computer Vision refers to the computer process to analyse visual data as a human would, and make inferences about what that data contains. When integrated in an application, these inferences can be turned into actionable responses. CV is increasingly being leveraged to solve many diverse real-world problems, ranging from security and health care to manufacturing, smart cities, and robotics.Bringing a CV application to production requires integrating several hardware and software components. CV deployment relies on cloud communication which is considered a flexible solution for machine learning applications, however mobile data transmission has high latency, is expensive and data in transmission poses a security risk. The alternative and increasingly popular option is to take CV applications to the edge. Edge or IoT devices are small and lightweight devices which a CV application can be deployed and run.The market for CV Edge devices is growing fast, in autonomous vehicles, manufacturing, health screening and military / civilian bodycams. However, the current technology with traditional slow processing and high-power consumption prevents the application of Artificial Intelligence (AI) to provide real-time insight from the environment they are interacting with. Instead they rely on humans or centralised Cloud computing, resulting in latency and poor reliability. In many theatres of operation timely decision making is critical as latency can result in life or death outcomes.Blueshift Memory's goal is to research the feasibility of a computing device to be used as a primary component for developers to deploy in CV and deliver AI applications on Edge. We expect that a Blueshift Memory design-enabled FPGA component will have a considerably faster calculation speed (5-10 times faster) and significantly lower energy consumption (30-50% reduction). The potentially lower price of our proposed design would make it possible to leverage existing IoT and CCTV camera infrastructure, providing a vitally important additional functionality.Blueshift Memory-enabled IoT devices will be able to perform tasks considered impossible in the past, Including weapon or suspicious object detection using bodycams or fixed CCTV. Police officers wearing our enabled AI CV bodycams could be alerted in real-time and the Operation Centre notified automatically when a weapon is detected. Identifying suspicious activity at the edge means only relevant data is transmitted to Central controllers, providing effective direction of resources to hot spots.

Blueshift内存正在寻求推动Edge设备上的下一代IoT计算机视觉（CV）应用程序的开发。计算机视觉指的是计算机过程，以分析人类的视觉数据，并推断该数据所包含的内容。当集成到应用程序中时，这些推论可以转变为可起作的响应。简历越来越多地被利用，以解决许多不同的现实世界问题，从安全性和医疗保健到制造业，智能城市和机器人。将简历应用程序纳入生产需要集成几个硬件和软件组件。简历部署依赖于云通信，而云通信被认为是机器学习应用程序的灵活解决方案，但是移动数据传输的延迟很高，昂贵，并且传输中的数据构成了安全风险。替代性且日益流行的选择是将CV应用程序带到边缘。 Edge或IoT设备是可以部署和运行简历应用的小型且轻巧的设备。CVEdge设备的市场在自动驾驶汽车，制造，健康筛查和军事 /民用体车上的速度快速增长。但是，当前具有传统缓慢处理和高功率消耗的技术阻止了人工智能（AI）的应用，以从与之互动的环境中提供实时见解。取而代之的是，他们依靠人类或集中式云计算，从而导致延迟和可靠性差。在许多行动剧院中，及时决策至关重要，因为潜伏期可能会导致生死结果。BlueshiftMemory的目标是研究将计算设备用作开发人员在CV中部署并在CV上提供AI应用程序的主要组件的可行性边缘。我们预计，蓝升记忆设计支持FPGA组件的计算速度将更快（5-10倍）和降低的能源消耗率明显降低（降低了30-50％）。我们提出的设计的潜在较低价格将使利用现有的物联网和CCTV摄像头基础架构，提供至关重要的额外功能。蓝调速度支持内存的物联网设备将能够执行过去被认为是不可能的任务，包括武器或可疑的任务使用车身车或固定CCTV检测对象检测。可以实时提醒穿着我们启用的AI CV车身的警察，并且在检测到武器时会自动通知操作中心。在边缘识别可疑活动意味着仅将相关数据传输到中央控制器，从而为热点提供了有效的资源方向。