Recent advances in artificial intelligence have enhanced the abilities of mobile robots in dealing with complex and dynamic scenarios. However, to enable computationally intensive algorithms to be executed locally in multitask robots with low latency and high efficiency, innovations in computing hardware are required. Here, we report TianjicX, a neuromorphic computing hardware that can support true concurrent execution of multiple cross-computing-paradigm neural network (NN) models with various coordination manners for robotics. With spatiotemporal elasticity, TianjicX can support adaptive allocation of computing resources and scheduling of execution time for each task. Key to this approach is a high-level model, “Rivulet,” which bridges the gap between robotic-level requirements and hardware implementations. It abstracts the execution of NN tasks through distribution of static data and streaming of dynamic data to form the basic activity context, adopts time and space slices to achieve elastic resource allocation for each activity, and performs configurable hybrid synchronous-asynchronous grouping. Thereby, Rivulet is capable of supporting independent and interactive execution. Building on Rivulet with hardware design for realizing spatiotemporal elasticity, a 28-nanometer TianjicX neuromorphic chip with event-driven, high parallelism, low latency, and low power was developed. Using a single TianjicX chip and a specially developed compiler stack, we built a multi-intelligent-tasking mobile robot, Tianjicat, to perform a cat-and-mouse game. Multiple tasks, including sound recognition and tracking, object recognition, obstacle avoidance, and decision-making, can be concurrently executed. Compared with NVIDIA Jetson TX2, latency is substantially reduced by 79.09 times, and dynamic power is reduced by 50.66%.

中文翻译：

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用于多智能任务机器人的时空弹性神经形态计算芯片

人工智能的最新进展增强了移动机器人处理复杂和动态场景的能力。然而，为了使计算密集型算法能够在低延迟和高效率的多任务机器人中本地执行，需要对计算硬件进行创新。在这里，我们报告了 TianjicX，一种神经形态计算硬件，可以支持多个跨计算范式神经网络 (NN) 模型的真正并发执行，并具有各种机器人协调方式。通过时空弹性，天机X可以支持计算资源的自适应分配和每个任务的执行时间调度。这种方法的关键是一个高级模型“Rivulet”，它弥合了机器人级要求和硬件实现之间的差距。它通过静态数据的分发和动态数据的流式抽象NN任务的执行，形成基本的活动上下文，采用时间和空间切片实现对每个活动的弹性资源分配，并进行可配置的混合同步异步分组。因此，Rivulet 能够支持独立和交互的执行。以Rivulet为基础，通过硬件设计实现时空弹性，开发了28纳米TianjicX事件驱动、高并行、低延迟、低功耗的神经形态芯片。使用单一的天机X芯片和专门开发的编译器堆栈，我们构建了一个多智能任务移动机器人天机猫，来执行猫捉老鼠的游戏。多项任务，包括声音识别和跟踪、物体识别、避障、和决策，可以同时执行。与NVIDIA Jetson TX2相比，延迟大幅降低79.09倍，动态功耗降低50.66%。