Augmenting Von Neumann's Architecture for an Intelligent Future

TL;DR

This paper introduces a new computer architecture that integrates a dedicated Reasoning Unit into the traditional Von Neumann model, enabling native artificial general intelligence capabilities through hardware-embedded symbolic inference and multi-agent coordination.

Contribution

It proposes a novel hardware architecture with a reasoning-specific instruction set, parallel symbolic pipelines, and integrated memory, facilitating intrinsic reasoning and learning for intelligent systems.

Findings

Enables goal-directed planning and dynamic knowledge manipulation.

Supports hybrid symbolic-neural computation at hardware level.

Establishes a foundation for general-purpose intelligent machines.

Abstract

This work presents a novel computer architecture that extends the Von Neumann model with a dedicated Reasoning Unit (RU) to enable native artificial general intelligence capabilities. The RU functions as a specialized co-processor that executes symbolic inference, multi-agent coordination, and hybrid symbolic-neural computation as fundamental architectural primitives. This hardware-embedded approach allows autonomous agents to perform goal-directed planning, dynamic knowledge manipulation, and introspective reasoning directly within the computational substrate at system scale. The architecture incorporates a reasoning-specific instruction set architecture, parallel symbolic processing pipelines, agent-aware kernel abstractions, and a unified memory hierarchy that seamlessly integrates cognitive and numerical workloads. Through systematic co-design across hardware, operating system, and agent runtime layers, this architecture establishes a computational foundation where reasoning, learning, and adaptation emerge as intrinsic execution properties rather than software abstractions, potentially enabling the development of general-purpose intelligent machines.