NimbleAI - Ultra-Energy Efficient and Secure Neuromorphic Sensing and Processing at the Endpoint

如今，在无处不在的物联网终端设备中只执行非常轻的人工智能处理任务，在这些设备中生成传感器数据，并且对能源的访问通常受到限制。然而，这种方法是不可扩展的，并且在安全性、隐私性、成本、能耗和延迟方面导致很高的代价，因为数据需要从端点设备传输到远程处理系统，例如数据中心。在能源消耗方面，效率低下尤其明显。为了跟上呈指数级增长的数据量（例如，视频）并允许与周围环境进行更先进、准确、安全和及时的交互，下一代端点设备将需要运行AI算法（例如，计算机视觉）和其它具有非常低等待时间的计算密集型任务（即，MS或更小的单位）和能量SNR（即，几十mW或更少）。NimbleAI将利用微电子和集成电路技术的最新进展，创建一个完整的神经形态传感处理解决方案，以便在资源和面积受限的芯片中有效地运行精确和多样化的计算机视觉算法。生物学将是NimbleAI的主要灵感来源，特别是专注于再现适应性和经验诱导的可塑性，使生物结构在处理动态视觉刺激时不断变得更有效。与最先进的技术相比，NimbleAI预计将有显著的改进（例如，商业上可获得的神经形态芯片），以及与实践状态相比至少100倍的能量效率提高和50倍的更短等待时间（例如，CPU/GPU/NPU/TPU处理基于帧的视频）。NimbleAI还将采取全面的方法来确保不同架构级别的安全性，包括硅级别。

Today only very light AI processing tasks are executed in ubiquitous IoT endpoint devices, where sensor data are generated and access to energy is usually constrained. However, this approach is not scalable and results in high penalties in terms of security, privacy, cost, energy consumption, and latency as data need to travel from endpoint devices to remote processing systems such as data centres. Inefficiencies are especially evident in energy consumption. To keep up pace with the exponentially growing amount of data (e.g., video) and allow more advanced, accurate, safe and timely interactions with the surrounding environment, next-generation endpoint devices will need to run AI algorithms (e.g., computer vision) and other compute intense tasks with very low latency (i.e., units of ms or less) and energy envelops (i.e., tens of mW or less). NimbleAI will harness the latest advances in microelectronics and integrated circuit technology to create an integral neuromorphic sensing-processing solution to efficiently run accurate and diverse computer vision algorithms in resource- and area-constrained chips destined to endpoint devices. Biology will be a major source of inspiration in NimbleAI, especially with a focus to reproduce adaptivity and experience-induced plasticity that allow biological structures to continuously become more efficient in processing dynamic visual stimuli. NimbleAI is expected to allow significant improvements compared to state-of-the-art (e.g., commercially available neuromorphic chips), and at least 100x improvement in energy efficiency and 50x shorter latency compared to state-of-the-practice (e.g., CPU/GPU/NPU/TPUs processing frame-based video). NimbleAI will also take a holistic approach for ensuring safety and security at different architecture levels, including silicon level.