Computing Architecture

In an alternate universe, computing architecture has evolved not around digital transistors and circuits, but artificial neural networks, where computation fundamentally resembles the processes of the human brain. This neuromorphic approach has dramatically changed how computers are built, interconnected and used.

To fully understand this neural network-based computing architecture, five major elements must be explored: distributed computing systems, the global neural network, decentralized and bioinspired data storage, energy efficiency, and the inclusion of quantum computing.

1. Neural network-based architecture

Artificial circuits that emulate the functionality of the human central nervous system form the basis of all computing devices. These neural networks offer extreme parallelism, making artificial intelligence (AI) applications not just possible, but inherent in the design. Since no clear distinction exists between software and hardware, programming principles differ significantly from traditional computing, closely resembling learning algorithms in real neural systems.

2. Distributed computing systems and the global neural network

In this alternate computing landscape, each device acts as a small, interconnected node within a vast distributed computing system. Unlike conventional centralized networks, the global neural network dynamically reconfigures and grows through organic self-organizing interactions between the connected nodes. This leads to an almost biological evolution of the overall architecture.

3. Decentralized and bioinspired data storage

Data storage also shares properties with the foundational neural network concept. No longer centralized within storage units, data is decentralized, existing in a distributed fashion across the entire network. Using bioinspired principles, information is encoded at the molecular level, with advanced DNA-based storage technologies adopted across the network.

4. Energy efficiency and the rejection of Moore's law

In sharp contrast to our universe where Microchip miniaturization follows Moore's law driving progress, in this alternate timeline, a radical shift in paradigm towards energy efficiency has taken the forefront of technological developments. By moving away from traditional CMOS technology and focusing on spintronics and photonic computing, the energy consumption drops significantly, enabling devices to operate in incredibly power-efficient manners.

5. Quantum computing and scientific discovery

Quantum computing, in this alternate universe, has become a pervasive and accessible technology used for solving complex scientific problems. The integration of neural-based architectures with quantum computing enables unprecedented advancements in physical simulations, molecular modeling, cryptography, and machine learning.

In summary, this alternate universe's computing architecture fundamentally diverges from our own, with neural network concepts as the core, driving an interconnected global network, decentralized data storage, energy efficiency, and pervasive quantum computing technologies. The unique properties of this neural computing landscape enable potential breakthroughs in artificial general intelligence, sustainability, and scientific discovery.