36th PATTAYA World Conference on Latest Computational Technologies (WCLCT-27)

Event Date : 2027-01-20 - 2027-01-22

Submission Deadline : 2027-01-01

Venue : Mercure Pattaya Ocean Resort (4-star hotel)

Website : https://eaceee.erpub.org/conference/238

อินทผลัมบาฮีสด หวาน กรอบ อร่อย

Call for papers/Topics

All Abstracts, Reviews, short articles, Full articles, Posters are welcomed related with any of the following research fields:

Part 1: Independent Computational Technologies

These are distinct paradigm shifts in how data is processed, stored, or secured, driven by unique physical principles or architectural designs.

Quantum Computing

• Superconducting Qubits and Ion-Trap Systems: The competing hardware architectures used to build quantum processors.

• Quantum Error Correction (QEC): Methods to mitigate \"noise\" and decoherence in Noisy Intermediate-Scale Quantum (NISQ) devices.

• Post-Quantum Cryptography (PQC): Developing mathematical algorithms that cannot be cracked by future quantum computers.

Neuromorphic and Brain-Inspired Computing

• Spiking Neural Networks (SNNs): Hardware mimicking the physical, time-dependent spikes of human brain neurons.

• Memristor-Based Architectures: Non-volatile memory components that process data directly where it is stored to eliminate transfer delays.

• Ultra-Low-Power Edge Intelligence: Designing chips that can process complex environmental data on milliwatts of power.

Biocomputing and DNA Data Storage

• DNA Digital Data Architecture: Encoding binary data (0s and 1s) into synthesized biological nucleotide sequences (A, T, C, G).

• Synthetic Biology Logic Gates: Programming living cells or synthetic proteins to act as organic processing units.

• Enzymatic Reading and Writing: Scale-up technologies for high-throughput, room-temperature molecular data retrieval.

Photonic and Optical Computing

• Silicon Photonics Interconnects: Replacing traditional copper wiring with laser-driven data channels to achieve light-speed data transfers.

• All-Optical Neural Networks (ONNs): Processing machine learning matrix math natively through optical lenses and beams without converting back to electricity.

• Laser-Driven Coherent Ising Machines: Specialized optical processors optimized to solve complex combinatorial problems instantly.

Part 2: Interrelated Computational Technologies

These technologies do not exist in a vacuum; they overlap, feed into one another, and create interdependent ecosystems.

Frontier Artificial Intelligence and High-Performance Compute (HPC)

• How they interrelate: Advanced AI models require hardware built specifically to handle trillions of mathematical operations simultaneously, transforming server architecture.

• Next-Generation Tensor and Graphics Processing Units: Hardware tailored for massive parallelization and mixed-precision arithmetic.

• Liquid-Cooling and Hyperscale Data Infrastructure: Thermal management techniques designed to survive the intense heat generated by modern AI training clusters.

• Automated Model Co-Design: Using AI algorithms to actively design and optimize the physical layout of the next generation of microchips.

Edge Computing and Distributed Internet of Things (IoT)

• How they interrelate: Centralized data centers cannot handle the sheer volume of global device data without lag, pushing computation to the absolute \"edge\" of the network.

• 5G Advanced and 6G Terahertz Communication: Ultra-low latency data pipelines acting as the nervous system for decentralized networks.

• Federated Learning on the Edge: Training AI models locally across thousands of consumer devices without uploading private user data to a central cloud.

• Real-Time Mesh Networks: Dynamic, self-healing communication webs formed by autonomous cars, drones, or smart infrastructure.

Decentralized Trust Systems and Advanced Cryptography

• How they interrelate: As computation becomes globally distributed and AI becomes more pervasive, verifying data integrity and maintaining privacy requires advanced cryptographic layers.

• Zero-Knowledge Proofs (ZKPs): Allowing a system to verify that a piece of information is true without revealing the actual data itself.

• Fully Homomorphic Encryption (FHE): Enabling cloud servers to run calculations on fully encrypted data without ever decrypting it first.

• Decentralized Autonomous Compute Networks: Crowdsourcing global, idle GPU and CPU power via blockchain-based coordination protocols.

Autonomous Robotics and Spatial Computing

• How they interrelate: Physical robots and mixed-reality headsets require a massive synthesis of edge AI, real-time spatial processing, and extreme low-latency tracking.

• Simultaneous Localization and Mapping (SLAM): Computational geometry algorithms that build 3D virtual maps of environments in real-time.

• Kinematic Edge AI: Embedding localized machine learning models into physical joints and limbs for real-world reflex actions.

• Neural Radiance Fields (NeRFs) and Gaussian Splatting: Transforming standard 2D camera feeds into highly detailed, interactive digital 3D scenes on the fly.

Organized by : EACEEE