The paper presents a rapid pipeline for training and deploying machine‑learning models on the WeBe Band, a wrist‑worn wearable device. It automates the creation of hardware‑efficient models, integrates with the Piccolo AI ecosystem, and supports OTA deployment while profiling latency and memory usage. Experimental results show trade‑offs between classical models and lightweight neural networks for real‑time performance on a microcontroller.
By Ehsan Kourkchi, Asmita Asmita, Houman Homayoun, Mahdi Eslamimehr
arXiv:2607. 16617v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to automate data-processing workflows, yet coding agents typically produce scripts that are not automatically materialized as persistent, editable platform artifacts.
By Runming He, Zhen Hao Wong, Hao Liang, Zimo Meng, Chengyu Shen, Xiaochen Ma, Wentao Zhang
arXiv:2607. 03574v1 Announce Type: cross Abstract: AI systems increasingly propose executable scientific models whose value depends on both their symbolic structure and their fitted continuous parameters.
By Lucas Sheneman
arXiv:2607. 02558v1 Announce Type: cross Abstract: As machine learning shifts from laboratory curiosity to critical infrastructure, the systems that sustain it span an extraordinary range, from sub-milliwatt microcontrollers to multi-gigawatt datacenter fleets.
By Vijay Janapa Reddi
The survey reviews how the open‑source RISC‑V ISA is being applied to machine learning, covering academic and commercial implementations, software frameworks, and real‑world applications. It presents a unified taxonomy of RISC‑V ML implementations, compares performance and design trade‑offs, evaluates toolchain maturity, and identifies emerging trends in instruction set extensions and specialized accelerators. The findings highlight progress in energy efficiency and framework integration, while noting challenges in standardization, verification, and ecosystem fragmentation, and propose four research directions to advance RISC‑V for next‑generation ML systems.
By Shriman Keshri, Apparna Singh, Chinmaya Kumar Palo, Shreya Adya, Subhankar Mishra
arXiv:2605. 23809v2 Announce Type: replace-cross Abstract: The Open Radio Access Network (O-RAN) architecture allows AI to be embedded directly into the RAN through modular xApps and rApps, yet creating these applications collecting data, training models, writing code, and deploying them safely remains slow and largely manual.
By Seyed Bagher Hashemi Natanzi, Pranshav Gajjar, Bo Tang, Vijay K. Shah