AQB8: Energy-Efficient Ray Tracing Accelerator through Multi-Level Quantization

Ray tracing (RT) is a rendering technique that produces high-fidelity images by simulating how light physically interacts with objects in a scene.This realism comes at a high computational and memory cost, largely driven by the need to find numerous ray-object intersections.To accelerate this process, scenes are typically structured using bounding volume hierarchy (BVH) trees, where objects are grouped within bounding boxes to minimize the number of required intersection tests.Specialized hardware, known as RT accelerators, accelerates BVH processing to boost computational speed, yet memory traffic persists as a major bottleneck, largely due to the bandwidth consumed by standard 32-bit floating-point (FP32) bounding boxes.Consequently, previous work has aimed to reduce memory traffic by compressing these boxes into low-bit (e.g., 8-bit) representations.However, existing compression techniques typically require decompressing bounding boxes back to FP32 for intersection tests, thus failing to eliminate the computational burden and energy cost associated with complex FP32 arithmetic during BVH traversal.This work introduces AQB8, an RT accelerator designed to operate on a quantized BVH tree constructed using a novel multi-level quantization technique.This approach enables RT to operate directly on low-bit integers using simpler, area-efficient hardware units, thereby drastically reducing the need for FP32 arithmetic during BVH traversal while mitigating overheads associated with reduced precision.As a result, across various scenes, AQB8 achieves a 70% reduction in DRAM accesses, a 49% reduction in energy consumption, a 27% hardware area reduction, and a 1.82x performance speedup over modern GPU RT accelerators.Our code is publicly available at https://github.com/nycu-caslab/AQB8.