  AI has led to significant advancements in computer vision and image
processing tasks, enabling a wide range of applications in real-life scenarios,
from autonomous vehicles to medical imaging. Many of those applications require
efficient object detection algorithms and complementary real-time, low latency
hardware to perform inference of these algorithms. The YOLO family of models is
considered the most efficient for object detection, having only a single model
pass. Despite this, the complexity and size of YOLO models can be too
computationally demanding for current edge-based platforms. To address this, we
present SATAY: a Streaming Architecture Toolflow for Accelerating YOLO. This
work tackles the challenges of deploying stateof-the-art object detection
models onto FPGA devices for ultralow latency applications, enabling real-time,
edge-based object detection. We employ a streaming architecture design for our
YOLO accelerators, implementing the complete model on-chip in a deeply
pipelined fashion. These accelerators are generated using an automated
toolflow, and can target a range of suitable FPGA devices. We introduce novel
hardware components to support the operations of YOLO models in a dataflow
manner, and off-chip memory buffering to address the limited on-chip memory
resources. Our toolflow is able to generate accelerator designs which
demonstrate competitive performance and energy characteristics to GPU devices,
and which outperform current state-of-the-art FPGA accelerators.
