Results 101 to 110 of about 24,567,669 (278)

ML-KEM (CRYSTALS-Kyber) on FPGA Using the Residue Number System

Cryptography
The NIST standardisation process for Post-Quantum Cryptography (PQC) has nominated the CRYSTALS-Kyber Key-Encapsulation Mechanism (KEM) scheme as the primary key establishment method.
Abdullah Alhassani, Mohammed Benaissa
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Residue Number System Reconfigurable Datapath [PDF]

, 2002
In this paper we describe a possible approach to implement a reconfigurable datapath for digital signal processing. The datapath should be programmable in terms of dynamic range, type and sequence of operations.
Gian Carlo Cardarilli, Andrea Del Re, Alberto Nannarelli, Marco Re   +3 more
core  

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

A blueprint for precise and fault-tolerant analog neural networks

Nature Communications
Analog computing has reemerged as a promising avenue for accelerating deep neural networks (DNNs) to overcome the scalability challenges posed by traditional digital architectures.
Cansu Demirkiran, Lakshmi Nair, Darius Bunandar, Ajay Joshi   +3 more
doaj   +1 more source

RRNS Arith Lib—Open-Source Redundant Residue Number System Arithmetics for Reliable Circuits

IEEE Open Journal of Circuits and Systems
A Residue Number System (RNS) represents integers through a set of residues obtained by integer division using a predefined set of pairwise coprime moduli. RNS is well-known for enabling efficient carry-free arithmetic and representing large numbers with
Tim Oberschulte, Enno Sievers, Holger Blume   +2 more
doaj   +1 more source

VLSI efficient RNS scalers and arbitrary modulus residue generators

, 2014
Carry propagation has been identified as the main timing bottleneck of the datapath elements of application-specific digital signal processors in the accustomed positional number system.
Low, Jeremy Yung Shern
core   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

A Hybrid Forward/Reverse Converter in Reversible Logic to Reduce Hardware Complexity of Residual Number System

Majlesi Journal of Electrical Engineering
As an emerging technology, reversible computing enables the development of high-performance computing systems with low energy consumption. A residue number system (RNS) that performs arithmetic operations in parallel with error tolerance and no carry ...
Ailin Asadpour, Amir Sabbagh, Azadeh Emrani   +2 more
doaj   +1 more source