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.

BluesShift Memory正在寻求推动下一代物联网计算机视觉(CV)应用程序在边缘设备上的开发。计算机视觉指的是像人一样分析视觉数据，并对数据包含的内容进行推断的计算机过程。当集成到应用程序中时，这些推论可以转化为可操作的响应。简历越来越多地被用来解决从安全和医疗保健到制造业、智能城市和机器人等各种现实世界的问题。将简历应用程序投入生产需要集成多个硬件和软件组件。CV部署依赖于云通信，云通信被认为是机器学习应用程序的灵活解决方案，但移动数据传输延迟高，成本高，传输中的数据存在安全风险。另一种越来越受欢迎的选择是将简历应用程序推向边缘。边缘或物联网设备是小巧轻便的设备，可以部署和运行CV应用程序。在自动驾驶汽车、制造、健康检查和军用/民用车身摄像头领域，CV Edge设备的市场正在快速增长。然而，目前的技术具有传统的处理速度慢和高功耗的特点，阻碍了人工智能(AI)的应用，以提供对他们正在交互的环境的实时洞察。相反，它们依赖人工或集中式云计算，导致延迟和可靠性差。在许多运营领域，及时的决策是至关重要的，因为延迟可能会导致生死结果。BluesShift Memory的目标是研究计算设备作为主要组件的可行性，以便开发人员在CV中部署并在Edge上交付AI应用程序。我们预计，支持蓝移存储器设计的FPGA组件将具有相当快的计算速度(快5-10倍)，并显著降低能耗(减少30%-50%)。我们提议的设计的潜在较低价格将使其能够利用现有的物联网和闭路电视摄像头基础设施，提供至关重要的额外功能。支持蓝移记忆的物联网设备将能够执行过去被认为不可能完成的任务，包括使用车载摄像头或固定闭路电视检测武器或可疑物体。佩戴我们启用的AI CV车载摄像头的警察可以实时发出警报，并在检测到武器时自动通知行动中心。识别边缘的可疑活动意味着只将相关数据传输到中央控制器，为热点提供有效的资源方向。