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Earlier sources: AI infrastructure
AI infrastructure comprises the compute, storage, serving systems, and operational tools used to train and deploy models. Follow inference engines, distributed training, observability, and the costs and constraints of running AI workloads.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗41 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- GitHub · Repository created · First archived
DaragonTech/LibLayaX ↗
Fast local AI decisions inside your application. A shared library and C API built on laya.cpp that runs the Laya typed-decision model in-process: no server, no Python. Windows, Linux and macOS, x64 and ARM64, CPU or GPU. An open-source alternative to Jev
- Hacker News · Posted on Hacker News · First archived
A functional taxonomy for LLM inference in agentic tasks ↗
Hacker News discussion.
- GitHub · Repository created · First archived
StayLameBro/backburner ↗
Your iPhone helps your Mac run a 27B model: faster prompt reading and more context over a USB-C cable
- GitHub · Repository created · First archived
ShadowSafin/Synapass ↗
AI inference gateway and control plane for multi-provider routing, policies, caching, tools, and observability.
- OpenAlex · Published · First archived
QUINN: Quantized Inference and Dataflow Characterization for Neural Networks on RISC-V Near-Memory Architectures ↗
Machine learning inference is moving to the edge due to latency, energy, and privacy constraints. The growing complexity of edge workloads places increasing pressure on memory hierarchies, making data movement a major limitation for both performance and energy efficiency. Fixed-function accelerators inherit the memory wall of the von Neumann model and lack the flexibility to follow evolving models. Programmable Vector Processing Unit (VPU)-based near-memory architectures alleviate this bottleneck using standard Complementary Metal-Oxide Semiconductor (CMOS) while remaining programmable, but st…
- arXiv · Published · First archived
A Missing Latent, Not a Missing Simulator: Radius-Augmented Inference for Real JWST Retrieval ↗
Amortized simulation-based inference (SBI), which is trained on radiative-transfer simulators, recovers exoplanet atmospheres accurately on synthetic James Webb Space Telescope (JWST) spectra but collapses when it comes to real reduced spectra. The flow posterior collapsed on real WASP-39b (importance-sampling effective sample size (ESS) = 1, best-fit \c{hi}2/N = 301), and such a failure is usually blamed on missing the forward-model physics, but ruling these levers out with nested sampling first (a temperature gradient, SO2 opacity, and a high-fidelity opacity set) leaves the fit unchanged, m…
- arXiv · Published · First archived
QuanVI: Score-based Variational Inference via Quantum Maximally Mixed States ↗
Score-based variational inference (VI) provides an alternative to Kullback--Leibler (KL)-based VI by minimizing the Fisher divergence between the variational distribution and the target. A prior score-VI approach formulates this optimization as an eigenvalue problem, with the variational distribution constructed from low-energy eigenstates. However, this eigenvalue-based formulation faces two high-dimensional obstacles: an intractably large parameter count due to exponential scaling and non-uniqueness of individual eigenvectors in degenerate or nearly degenerate low-energy subspaces. We propos…
- arXiv · Published · First archived
MoSE: Mode-Switching Expander for Mixed LLM Training and Inference ↗
AI clusters increasingly run large language model (LLM) inference and training on the same fabric. Prefill-decode (P-D) disaggregation creates key-value (KV) cache transfers between prefill and decode groups, whereas training collectives and all-to-all traffic benefit from near-uniform global connectivity. A static sparse topology can therefore be poorly matched to one of the two traffic patterns. We present Mode-Switching Expander (MoSE), a reconfigurable expander that treats topology design as a fixed-degree edge-allocation problem. MoSE reallocates the same sparse edge budget toward direct…
- arXiv · Published · First archived
SparLeak: Privacy Leakage from Sparse Attention in LLM Inference on Shared GPUs ↗
Sparse attention is widely used to accelerate long-context inference in modern large language models (LLMs), but its input-dependent execution behavior introduces previously unexplored privacy risks. We identify a new GPU micro-architectural side channel, termed Sparsity-Induced Memory Access (SIMA), which arises from secret-dependent key-value cache access patterns induced by sparse attention. Based on this observation, we present SparLeak, a phase-aware side-channel attack that extracts SIMA traces during LLM inference and enables two practical privacy extractions: query attribute inference…
- OpenAlex · Published · First archived
Reducing spiking neural network inference latency for behavior cloning with input adaptive leakage ↗
Abstract Behavior cloning enables efficient imitation of complex human skills from a limited number of demonstrations. However, artificial neural networks (ANNs), which are commonly employed for policy learning, suffer from their high computational demands and energy consumption. Spiking neural networks (SNNs) offer a promising alternative because of their event-driven and energy-efficient characteristics. A common training approach for SNNs is to convert the weights from a pretrained ANN into an equivalent SNN. However, due to rate coding, such a converted SNN typically requires multiple infe…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about AI infrastructure
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.