NimblaAI: ULTRA-ENERGY EFFICIENT AND SECURE NEUROMORPHIC SENSING AND PROCESSING AT THE ENDPOINT

NimblaAI：端点的超节能且安全的神经形态传感和处理

• 批准号: 10039059
• 金额: $ 20.6万
• 依托单位: QUEEN MARY UNIVERSITY OF LONDON
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10039059
• 关键词: NimblaAI; ULTRA; ENERGY; EFFICIENT; SECURE

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 notscalable and resultsin high penaltiesin terms ofsecurity, 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.

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端点设备中执行，其中传感器数据的生成和能源的获取通常受到限制。然而，这种方法不可扩展，并且由于数据需要从端点设备传输到数据中心等远程处理系统，因此在安全性、隐私性、成本、能耗和延迟方面会产生很高的代价。效率低下在能源消耗方面尤为明显。为了跟上指数级增长的数据量（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人工智能算法（如计算机视觉）和其他计算密集型任务，并且延迟极低（即毫秒或更少）和能量包膜（即几十兆瓦或更少）。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，在资源和区域受限的终端设备芯片中有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（例如，商用神经形态芯片）相比，NimbleAI有望实现重大改进，并且与目前的实践（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还将采取整体方法，确保包括硅级在内的不同架构级别的安全性。