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Earlier sources: Hardware
Hardware is the physical foundation of computing: chips, sensors, devices, networks, and the processes that manufacture them. Follow semiconductor research, compute architectures, and engineering developments alongside their software and energy connections.
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 ↗527 classified signals in the fourteen-day window. Recent momentum: steady. 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.
- arXiv · Published · First archived
Evaluating LLMs for Translating Legacy Fortran Atmospheric Physics to GPU-Accelerated JAX ↗
Legacy Fortran physics packages in atmospheric models such as CESM, WRF, and E3SM pose a barrier to GPU modernization, while manual porting requires accelerator-programming expertise. We present LAFT (LLM-Assisted Fortran Translation), an agentic framework for converting Fortran atmospheric physics parameterizations into GPU-accelerated JAX while preserving numerical agreement with the Fortran reference. LAFT coordinates source-code analysis, translation, repair, runtime validation, and performance profiling. It combines a deterministic front end for metadata extraction and bridge generation,…
- arXiv · Published · First archived
GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping ↗
Globally consistent, real-time state estimation in large-scale, perceptually degraded environments is essential for autonomous vehicles and aerial robots, and requires fusing LiDAR, inertial, and GNSS measurements. Existing fusion methods, however, share a scan-to-map front-end with two failure modes. First, each scan is aligned to an incrementally built map that drifts under degeneracy, and once the estimate diverges the error is irrecoverable. Second, even without divergence, a registration biased by dynamic objects or wrong correspondences is propagated as a single pose constraint with an o…
- arXiv · Published · First archived
Toward Intelligent Networks via AI-Aware GPU-Native Packet Processing ↗
The integration of inline Artificial Intelligence (AI) models into critical network infrastructure is fundamentally bottlenecked by the high latency and synchronization overhead of CPU-mediated packet processing. While legacy GPU offload and recent CPU-bypass frameworks attempt to bridge this gap, they remain trapped in proprietary ecosystems or still rely on the host CPU and coarse-grained batching to coordinate stateful telemetry and AI pipeline execution. In this paper, we propose AGP, a novel framework that promotes the GPU from a passive accelerator to a primary data-path controller, answ…
- Hacker News · Posted on Hacker News · First archived
Next BIG PC upgrade isn't a GPU Its a monitor ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Garlic-peel sensor powers low-cost home-security sensor ↗
Hacker News discussion.
- arXiv · Published · First archived
On-chip filled array hyperspectral kinetic inductance detectors (FHYKID) for line intensity mapping ↗
The next generation Line Intensity Mapping (LIM) experiments will require very high throughput instrumentation, and as a consequence high pixel counts focal planes. For the study of the [CII] line in the redshift range z=7-8, we develop a hyperspectral filled focal plane (FHYKID) operating in the range 210-235 GHz and with a spectral resolution reaching 120. The spectral filtering is achieved thanks to planar Slot Split Ring Resonators (SSRR) coupled to Lumped Element Kinetic Inductance Detectors (LEKID). This robust concept achieves, thanks to its simplicity, large quantum efficiency and is s…
- arXiv · Published · First archived
Protecting CPU AI On Edge TEEs: WebAssembly's Promise and Practical Challenges ↗
AI models on edge hardware contain important intellectual property (IP), but an adversary can steal it when they achieve root access. Trusted Execution Environments (TEE) like Arm TrustZone protect against these Operating System (OS) level attacks. However, they are challenging to use. More precisely, it is difficult to run unaltered applications inside a TEE. This work presents a solution that allows the execution of unaltered AI models, compiled to WebAssembly, on the WebAssembly Micro Runtime (WAMR) in OP-TEE for Arm TrustZone. Additionally, this work provides an AI model distributor that e…
- GitHub · Repository created · First archived
vistorx/CatWandConnect ↗
Open-hardware cat toy robot: an ESP32 drives a wand on two servos. Standalone firmware with its own web interface, browser flashing and optional Home Assistant integration.
- Hacker News · Posted on Hacker News · First archived
Show HN: Phobos – a tiny kernel language that now runs LLMs on a 2019 GPU ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Ask HN: Are we trading model capacity for compute? ↗
Do you think we're trading off model size, and model capacity, for compute? Take looped transformers, which GPT-6 Astra is rumored to be based on. They increase compute while keeping the model's capacity almost fixed. So my question is, if we had an infinitely large model (with also infinite capacity) and of course infinite compute, would we still need CoT or looped transformers? Could we have a model where the reasoning happens directly within a…
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How to assess evidence about Hardware
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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