Optimising AI reasoning pipeline – Layer 2 + 3 merge for CPU efficiency

[mohammad.manuar]

Environment

TensorRT Version: 8.8
GPU Type: NVIDIA A100 40GB
Nvidia Driver Version: 535.54
CUDA Version: 12.1
CUDNN Version: 8.9
Operating System + Version: Ubuntu 22.04 LTS
Python Version: 3.11
TensorFlow Version: 2.14 (if applicable)
PyTorch Version: 2.1 (if applicable)
Baremetal or Container: Container (nvidia/cuda:12.1-cudnn8-runtime-ubuntu22.04)

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Relevant Files

• GitHub Issue link with full micro-proof: https://github.com/your-repo/AI_CPU_LayerMerge_Optimisation
• Attached PDF: AI_CPU_LayerMerge_Optimisation_v1.pdf (diagram + pseudo-code + extended description)

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Steps To Reproduce

1. Input scenario: multi-layer AI reasoning pipeline with redundant Layer 2 (Core) + Layer 3 (Redundant Checks)
2. Apply short pseudo-code (example inline):

def layer2_optimized(input):
    return merge_logic(process_base(input))

[athkumar]

Hi @mohammad.manuar, Please check the shared github repo link. It’s broken. The url has your-repo/ . Share the correct URL and PDF link AI_CPU_LayerMerge_Optimisation_v1.pdf to let us help you.

Thank You.