4 papers
Reading AI Model Compilation in MLIR Through the Lens of Formal Theories
Javed Absar
Compiler infrastructures such as MLIR rest on a set of design principles: IR abstractions, interfaces, match-and-rewrite, flow analysis, type conversion, staged lowering, and so on…
Hexagon-MLIR: An AI Compilation Stack For Qualcomm's Neural Processing Units (NPUs)
Mohammed Javed Absar, Muthu Baskaran, Abhikrant Sharma +22
In this paper, we present Hexagon-MLIR,an open-source compilation stack that targets Qualcomm Hexagon Neural Processing Unit (NPU) and provides unified support for lowering Triton…
Analyzing Latency Hiding and Parallelism in an MLIR-based AI Kernel Compiler
Javed Absar, Samarth Narang, Muthu Baskaran
AI kernel compilation for edge devices depends on the compiler's ability to exploit parallelism and hide memory latency in the presence of hierarchical memory and explicit data mov…
Tensor Evolution: A Framework for Fast Evaluation of Tensor Computations using Recurrences
Javed Absar, Samarth Narang, Muthu Baskaran
This paper introduces a new mathematical framework for analysis and optimization of tensor expressions within an enclosing loop. Tensors are multi-dimensional arrays of values. The…