Merge pull request #308 from rod409/master

add automotive rules to policy

inference_rules.adoc

@@ -184,6 +184,7 @@ Each sample has the following definition:
 |Mixtral-8x7B            |one sequence
 |RGAT	            |one node id
 |Llama3.1-405B	            |one sequence
+|PointPainting             | five images and one lidar point cloud
 |===
 
 == Benchmarks
@@ -289,6 +290,7 @@ The Edge suite includes the following benchmarks:
 |Language |Language processing |BERT |SQuAD v1.1 (max_seq_len=384) | 10833 | 99% of FP32 and 99.9% of FP32(f1_score=90.874%)
 |Language |Summarization |GPT-J |CNN Dailymail (v3.0.0, max_seq_len=2048) | 13368 | 99% of FP32 and 99.9% of FP32 (rouge1=42.9865, rouge2=20.1235, rougeL=29.9881). Additionally, for both cases the generation length should be more than 90% of the reference (gen_len=4016878)
 |Generative |Text to image |SDXL |Subset of coco-2014 val | 5000 |FID range: [23.01085758, 23.95007626] and CLIP range: [31.68631873, 31.81331801]
+|Automotive | 3D Object Detection | PointPainting | Waymo Open Dataset | 1024 | 99.9% of FP32 (0.5425 mAP)
 |===
 
 Each Edge benchmark *requires* the following scenarios, and sometimes permit an optional scenario:
@@ -301,6 +303,7 @@ Each Edge benchmark *requires* the following scenarios, and sometimes permit an
 |Language |Language processing |Single Stream, Offline
 |Generative |Text to image |Single Stream, Offline
 |Language |Summarization |Single Stream, Offline
+|Automotive | 3D object detection | Single Stream with 99.9% tail latency
 |===