Resumen
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 1OOx improvement in energy efficiency and 5Ox shorter latency compared
Us processing frame-based video). NimbleAI will also take a holistic approach for
nsuring safety and security at different architecture levels, including silicon level.
