Neuton is a framework that automatically converts neuron‑level abstract interpreter specifications into tensor‑level executables, enabling the creation of new DNN certifiers without manual tensor programming. It introduces a stack‑based intermediate representation with shape analysis, domain‑specific rewrite rules for efficient tensor operations, and a novel sparse tensor format (gBCSR) tailored to certifier sparsity. The authors demonstrate that Neuton preserves neuron‑level semantics and evaluate it on eight certifiers across 32 DNN benchmarks, compiling four existing certifiers and producing executable implementations for four others, revealing new precision‑runtime trade‑offs such as SkipPoly matching DeepPoly’s precision while often running faster.
arXiv:2606. 09213v1 Announce Type: cross 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 paper introduces Numbat, a self‑contained machine‑learning stack implemented entirely in Zig with no external runtime dependencies. It covers tensor computation, automatic differentiation, neural‑network modules, mixed precision, multi‑GPU training, data loading, and monitoring, and exposes a stable C ABI with over 1,400 entry points and bindings for six languages. The authors verify the stack against a reference implementation at multiple levels, uncovering ten silent recipe divergences, and demonstrate its practical capability by training a 25.9M‑parameter YOLOv8m detector on COCO 2017, achieving a competitive mAP score and matching single‑GPU performance.
The paper explores using large language models (LLMs) to replace traditional compiler backends, a process termed AI lowering. An LLM agent translates Triton kernels directly into NVIDIA PTX, achieving 0.83x–3.34x the performance of autotuned Triton on a variety of GPUs and ML kernels. The study also extends a PTX verifier to support modern GPU features, highlighting the potential for AI compilers to reduce engineering effort for new hardware.
arXiv:2608. 00029v1 Announce Type: cross Abstract: The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware.
arXiv:2604. 09558v2 Announce Type: replace-cross Abstract: With the widening gap between compute and memory operation latencies, data movement optimizations have become increasingly important for DNN compilation.
arXiv:2608. 12004v1 Announce Type: cross Abstract: In modern AI frameworks, GPU kernels are key to overall system performance.
arXiv:2606. 19279v1 Announce Type: new Abstract: Neurosymbolic semantics is fragmented: classical, fuzzy, probabilistic and neural systems each define truth by their own inductive rules.
SCOPE compiles natural‑language policies into neurosymbolic predicate programs that combine small neural models, deterministic extractors, and logical composition. The system is jointly optimized with adversarially generated counterexamples, achieving higher accuracy than prompt‑optimization, task‑decomposition, and workflow‑generation baselines on real‑world policy benchmarks. Programs trained with the weakest executor transfer to other models, enabling deployment on inexpensive models with stable, interpretable predictions.
arXiv:2608.25536v2 Announce Type: replace-cross Abstract: In this work, we propose a view on electronic Neuromorphic Design Automation (eNDA), which we see as a design automation flow that bridges co...
arXiv:2603. 23878v3 Announce Type: replace-cross Abstract: The parameterized CROWN analysis, a.
arXiv:2601. 16622v2 Announce Type: replace-cross Abstract: Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems.
arXiv:2608. 08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing.