MLIR
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Related Articles from SNS
SNN-MLIR: An MLIR Dialect for Compiling Neuromorphic SNNs from NIR to Bare-Metal C
arXiv:2606.09213v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are increasingly trained in a wide range of frameworks (SnnTorch, Lava, Norse, and others) each with its own model format. The Neuromorphic Intermediate Representation (NIR) addresses this fragmentation by providing a common, framework-independent format for exchanging trained SNN models. NIR solves the exchange problem, but it stops there.
MIMO: Multilingual Information Retrieval via Monolingual Objectives
Announce Type: new Abstract: Multilingual Information Retrieval (MLIR) reflects real-world search environments in which queries and relevant documents may appear in different languages within a mixed-language corpus. However, existing embedding models are primarily optimized for Multi-Monolingual retrieval and their performance often degrades in MLIR settings. Moreover, directly applying conventional contrastive learning to MLIR can exacerbate language clustering and expose a trade-off...
Fixed-Point Scaffolding in the Clef Programming Language
Announce Type: new Abstract: For fans of Gabriel's "Worse is Better" it may be ironic that C++, by way of MLIR, serves as the scaffold for compiling an ML-family language whose correctness properties are structural. A crucial intersection in our Composer compiler initiates its lowering with a fixed-point combinator that preserves the dimensional, grade, escape, and numeric-representation structure from the Program Semantic Graph. And the MLIR that's witnessed from the PSG is no passive host.
Tensor Algebraic Property Skeletons: Amplifying Property-Based Testing for AI Compilers
Announce Type: new Abstract: Deep learning (DL) compilers such as TVM and ONNX-MLIR lower tensor computation graphs into optimized executables for target backends. Testing these AI compilers has made substantial progress in generating well-formed inputs in the context of fuzzing; however, such generation alone does not catch semantic drifts from algebraic invariants that graph transformations and optimizations are expected to preserve. While tensor algebra has been studied for decades, it...