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

如今，只有非常轻的人工智能处理任务在无处不在的物联网终端设备中执行，这些设备生成传感器数据，能源访问通常受到限制。然而，由于数据需要从终端设备传输到远程处理系统(如数据中心)，这种方法不可扩展，并且在安全、隐私、成本、能耗和延迟方面会导致很高的惩罚。低效在能源消耗方面尤为明显。为了跟上呈指数级增长的数据量(例如视频)的步伐，并允许与周围环境进行更先进、准确、安全和及时的交互，下一代终端设备将需要以非常低的延迟(即毫秒或更低的单位)和能量包络(即数十兆瓦或更小)运行人工智能算法(例如计算机视觉)和其他计算密集型任务。NimbleAI将利用微电子和集成电路技术的最新进展，创建一种集成的神经形态传感处理解决方案，以便在用于终端设备的资源和面积受限的芯片上高效运行准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，特别是专注于复制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方面不断变得更高效。与最先进的(例如，商用的神经形态芯片)相比，NimbleAI有望实现显著改进，与最先进的实践(例如，CPU/GPU/NPU/TPU处理基于帧的视频)相比，能效至少提高100倍，延迟缩短50倍。NimbleAI还将采取全面的方法来确保不同架构级别的安全和保障，包括硅片级。

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.