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

Blueshift Memory正在寻求推动边缘设备上下一代物联网计算机视觉（CV）应用的开发。计算机视觉是指计算机像人类一样分析视觉数据的过程，并对数据包含的内容进行推断。当集成到应用程序中时，这些推断可以转化为可操作的响应。CV越来越多地被用于解决许多不同的现实问题，从安全和医疗保健到制造业、智能城市和机器人技术。将CV应用程序引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的灵活解决方案，然而移动的数据传输具有高延迟，成本高，并且传输中的数据会带来安全风险。另一种越来越受欢迎的选择是将简历应用程序带到边缘。边缘设备或物联网设备是可以部署和运行CV应用程序的小型轻量级设备。CV边缘设备的市场正在快速增长，包括自动驾驶汽车，制造业，健康筛查和军用/民用人体摄像头。然而，目前传统的处理速度慢和功耗高的技术阻碍了人工智能（AI）的应用，无法从与之交互的环境中提供实时洞察。相反，它们依赖于人类或集中式云计算，导致延迟和可靠性差。在许多战区，及时决策至关重要，因为延迟可能会导致生死攸关的结果。Blueshift Memory的目标是研究将计算设备用作开发人员在CV中部署并在Edge上交付AI应用程序的主要组件的可行性。我们预计，支持Blueshift Memory设计的FPGA组件将具有更快的计算速度（快5-10倍）和更低的能耗（降低30-50%）。我们提出的设计可能更低的价格将使利用现有的物联网和闭路电视摄像机基础设施成为可能，提供至关重要的额外功能。支持Blueshift Memory的物联网设备将能够执行过去被认为是不可能的任务，包括使用人体摄像机或固定闭路电视进行武器或可疑物体检测。佩戴我们启用的AI CV人体摄像头的警察可以实时收到警报，并在检测到武器时自动通知操作中心。在边缘识别可疑活动意味着只有相关数据才能传输到中央控制器，从而有效地将资源引导到热点。

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.