In this article, I am expanding the basic pipeline from the previous article to support a Linear Layer (the foundational building block of modern neural networks). At the hardware level, this workload is driven by a GeMM (General Matrix Multiply) operation, traditionally defined as: C ← αAB + βC To achieve this, I added two new operations to the compiler: relu and matmul. Since relu shares a similar element-wise structure to the 'add' operator covered in Part 1, this article

MLIR Lowering Pipeline Lowering Strategy (High-Level Overview) The lowering pipeline looks like this: ONNX graph -> custom mlir dialect -> tensor (value) -> memref (memory) -> llvm -> JIT Full code for this article is available here This article's focus is only building an end-to-end skeleton that takes a high-level ONNX model and lowers it through progressively more concrete representations until it executes via a JIT engine. I kept the initial scope to scalar and 1D vector

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Github Page for full code: https://github.com/eyereece/ai-rag-experiments/tree/main/chat-with-your-pdf-ai Overview: Virtual Environment...