Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
The growth of Edge AI and Tiny Machine Learning (TinyML) brings up a big problem between hardwareprice and processing power. Single Board Computers (SBCs) like the Raspberry Pi have enough computingpower for many tasks. However, their high price of ₱1,960 to ₱5,600+ and power draw of 2.5W to 15W makethem too expensive for battery powered IoT sensor networks. Microcontrollers like the ESP32 series aremuch cheaper at ₱110 to ₱280, but they have small SRAM and limited processing speed. This paper connectsElectronic Design Automation (EDA) logic tools with the cost of Edge AI. Based on the study by Astillero(2026), which uses Graph Neural Networks (GNNs) and Reinforcement Learning (RL) to reshape logiccircuits, we show that cutting down power and gate area at the circuit level helps run TinyML models oncheap sub-₱60 silicon chips. The results show that AI based logic pruning cuts dynamic power by up to 64%and gate area by up to 42%. This proves that AI assisted EDA makes Edge AI setups cheaper and morepractical for real world use.
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoptio...
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
A comprehensive taxonomy of the challenges faced when a medium-scale organization decided to adopt software platforms is provided, namely: business challenges, organizational challenges, technical challenges, and people challenges.
Yaser Ghanam, F. Maurer, P. Abrahamsson· Information and Software Tec...· 41 citations· ⚡3
It is shown that high article processing charges are not sufficiently justified by the publishers, which often lack transparency and may prevent authors from adopting OA.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· Scientometrics· 21 citations· ⚡1
MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently, offers an effective and efficient solution for PPI overall property predictions.
Yang Yue, Shu Li, Yihua Cheng et al.· bioRxiv· 15 citations
PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.
Si-Long Zhai, Huifeng Zhao, Ji-Ke Wang et al.· Journal of Chemical Informat...· 13 citations· ⚡1
OmniMol is presented, a framework using hypergraphs to improve predictions of molecular properties, addressing challenges of imperfect data annotation and enhancing model explainability, and achieves state-of-the-art performance in properties prediction.
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
