论文标题
KRAKEN：一种直接的事件/基于框架的多传感器融合SOC
Kraken: A Direct Event/Frame-Based Multi-sensor Fusion SoC for Ultra-Efficient Visual Processing in Nano-UAVs
论文作者
论文摘要
小型无人驾驶汽车（UAV）有可能显着提高安全性并降低关键基础设施维护和污水架后搜索和救援等应用的成本。许多场景都需要无人机向纳米和PICO大小的形式缩小。实现纳米UAV的真正自主权的主要开放挑战是在对象检测，跟踪，导航和障碍物中运行复杂的视觉任务，以高速和鲁棒性在紧密的有效负载和功率约束下完全避免。借助22NM FDX技术制造的Kraken SoC，我们展示了一种多视觉传感器功能，可利用基于事件的和BW/RGB成像仪，将其输出组合为以前在纳米-UAV的单个低功率芯片上不可能进行的多功能视觉任务。 Kraken是一种超低功率，异质的SOC架构，集成了三个加速发动机和大量的外围设备，可有效地与基于标准的基于框架的传感器和新颖的基于事件的DVS。 Kraken可以在专用的神经形态能量培训加速器上推断事件驱动的稀疏事件驱动的子UJ/INF SNN推断。此外，它可以通过将1.8top \ s \ w 8核RISC-V处理器群集与混合精液DNN扩展与1036TOP \ s \ w} TNN加速器相结合，从而执行基于帧的推理。
Small-size unmanned aerial vehicles (UAV) have the potential to dramatically increase safety and reduce cost in applications like critical infrastructure maintenance and post-disaster search and rescue. Many scenarios require UAVs to shrink toward nano and pico-size form factors. The key open challenge to achieve true autonomy on Nano-UAVs is to run complex visual tasks like object detection, tracking, navigation and obstacle avoidance fully on board, at high speed and robustness, under tight payload and power constraints. With the Kraken SoC, fabricated in 22nm FDX technology, we demonstrate a multi-visual-sensor capability exploiting both event-based and BW/RGB imagers, combining their output for multi-functional visual tasks previously impossible on a single low-power chip for Nano-UAVs. Kraken is an ultra-low-power, heterogeneous SoC architecture integrating three acceleration engines and a vast set of peripherals to enable efficient interfacing with standard frame-based sensors and novel event-based DVS. Kraken enables highly sparse event-driven sub-uJ/inf SNN inference on a dedicated neuromorphic energy-proportional accelerator. Moreover, it can perform frame-based inference by combining a 1.8TOp\s\W 8-cores RISC-V processor cluster with mixed-precision DNN extensions with a 1036TOp\s\W} TNN accelerator.
