Autonomous driving stacks have shifted from 2D convolutional networks to multi-camera Bird's Eye View (BEV) Transformer architectures. This transition demands hardware evaluation beyond peak TOPS marketing claims, testing memory bandwidth limits and compiler quantization fidelity.
While a chip may boast 200 peak TOPS on dense GEMM operations, complex layer normalization and cross-attention kernels frequently stall processing units waiting for DRAM operands. In our benchmarks, memory-bound architectures achieved less than 35% compute utilization on BEV architectures without optimized compiler fusion.
Converting floating point networks to INT8 requires calibration against representative automotive drive scenarios. While bounding box classification remains intact, aggressive post-training quantization can cause up to a 6 percent degradation in distance estimation for distant pedestrians unless Quantization-Aware Training (QAT) is utilized during model development.