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

blushift Memory正在寻求推动边缘设备上下一代物联网计算机视觉（CV）应用程序的开发。计算机视觉是指像人类一样分析视觉数据，并对数据内容进行推断的计算机过程。当集成到应用程序中时，这些推断可以转化为可操作的响应。CV正越来越多地被用于解决各种各样的现实问题，从安全和医疗保健到制造业、智能城市和机器人。将CV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，这被认为是机器学习应用的灵活解决方案，但移动数据传输具有高延迟，昂贵且传输中的数据存在安全风险。另一种日益流行的选择是将简历应用程序推向边缘。边缘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。在自动驾驶汽车、制造业、健康检查和军用/民用随身摄像头等领域，CV Edge设备的市场正在快速增长。然而，目前传统的处理速度慢、功耗高的技术阻碍了人工智能（AI）的应用，使其无法从与之交互的环境中提供实时洞察。相反，它们依赖于人工或集中式云计算，导致延迟和可靠性差。在许多手术室，及时的决策是至关重要的，因为延迟可能导致生命或死亡的结果。blushift Memory的目标是研究一种计算设备的可行性，该设备可作为开发人员在CV中部署的主要组件，并在Edge上提供人工智能应用程序。我们期望启用蓝移内存设计的FPGA组件将具有相当快的计算速度（快5-10倍）和显着降低的能耗（降低30-50%）。我们提出的设计潜在的较低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，提供一个至关重要的额外功能。启用蓝移内存的物联网设备将能够执行过去认为不可能完成的任务，包括使用随身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随身摄像机的警察可以实时收到警报，当检测到武器时，操作中心会自动通知。识别边缘的可疑活动意味着只将相关数据传输给中央控制器，从而有效地将资源定向到热点。

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.