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Project details

Parallel Computing Paradigms for Next-Gen AGI

🧠 Overview

The future of Artificial General Intelligence (AGI) demands not only algorithmic innovation but also foundational changes in the hardware architectures that support symbolic reasoning, recursive inference, and cognitive adaptability. This project aims to systematically explore non-traditional and emerging hardware paradigms capable of delivering parallelism, efficiency, and scalability beyond what is achievable with current architectures.

We focus on the development and evaluation of alternative computing models—specifically analog computing, in-memory processing, temporal logic, and race logic systems—for their potential in supporting symbolic AGI systems built upon frameworks like Hyperon and MeTTa.

This proposal is backed by our established platforms, the Symbolic AGI Toolkit and the CQ Tester, which together offer symbolic test environments, FSM/Regex pipelines, cognitive state representations, and feedback-driven motivation systems. These tools will be leveraged to model AGI workloads and test how emerging hardware paradigms cope with symbolic execution, memory transformation, and multi-agent goal regulation.

🔍 Problem Space and Motivation

Conventional computing platforms (CPU/GPU/TPU) are inherently limited in symbolic concurrency and memory-driven feedback processing. AGI frameworks that incorporate attention mechanisms, recursive memory updating, and high-fidelity symbolic knowledge graphs demand massive symbolic throughput with energy-efficient, low-latency access to dynamically evolving state spaces.

This project will determine the feasibility and promise of parallel symbolic processing across emerging paradigms, evaluating how they:

• Scale in response to symbolic feedback-driven learning,

• Process FSM-based transitions and Regex queries in parallel,

• Manage large-scale cognitive state evolution (D-K-R structures),

• Align with MeTTa-based execution and Hyperon-style attention networks.

🔬 Research Goals

1. Identify viable computing paradigms beyond digital silicon that can support symbolic logic structures, particularly those that can simulate or execute FSM chains and Regex interpretations natively or efficiently.

2. Benchmark their cognitive suitability across representative AGI tasks: recursive reasoning, probabilistic feedback cycles, and parallel attention flows.

3. Prototype at least one system with partial implementation of symbolic cognitive routines (e.g., FSM traversal or Regex filtering across attention graphs).

4. Evaluate symbolic architecture fit—how well do the explored paradigms serve key AGI building blocks such as:

   • MeTTa symbolic ruleset processing

   • D-K-R state loops from CQ Tester

   • Goal-shift logic from CQ-based motivational agents

⚙️ Methodological Framework

Our research methodology involves:

• Symbolic encoding of AGI tasks using the Symbolic AGI Toolkit and CQ Tester

• Translation of these symbolic programs into a minimal FSM and Regex model

• Execution (or simulation) of these models across various paradigms via co-design or hardware prototyping

• Measurement of symbolic throughput, latency, concurrency, and energy cost

• Evaluation against MeTTa interpreters, ECAN resource flow models, and DAS-like attention-memory integrations

All comparisons will be quantitative (performance, energy, concurrency) and qualitative (symbolic alignment, compatibility, generalizability).

📘 Milestone Structure

Milestone 1: Identification of Hardware Paradigms and Research Plan (25%)

• Map symbolic execution models to hardware paradigms

• Design experimental goals, cognitive workloads, and selection criteria

Milestone 2: Literature Review and Benchmarking Criteria (25%)

• Focused review on analog, in-memory, temporal, and race logic systems

• Formalize benchmarking criteria using FSM/Regex pipelines and CQ agent behaviors

Milestone 3: Initial Experiments and Feasibility Testing (25%)

• Simulate or co-design hardware logic for AGI symbolic routines

• Analyze symbolic task performance, bottlenecks, and hardware fit

Milestone 4: Final Evaluation and Prototype Demonstration (25%)

• Present best-fit architecture for AGI symbolic acceleration

• Deliver small-scale prototype and integration concept for MeTTa + ECAN loop

🌐 Integration Path & Broader Impact

This project contributes directly to decentralized, transparent AGI development by mapping symbolic cognition workloads onto novel physical substrates. Our findings will lay the groundwork for building next-generation interpretable AGI accelerators. The deliverables will also shape future iterations of Hyperon/MeTTa-compatible hardware.

Open Source Licensing

© 2025 Accelflare. All rights reserved.

This work is released under a restricted-access dual license:

🔍 Research Use:  
Permitted for non-commercial research and academic evaluation, with attribution. Derivative works must remain open and traceable.

💼 Commercial Use:  
All commercial use—including integration into proprietary systems or monetized platforms—requires a separate licensing agreement. Contact: licensing@accelflare.com

No resale, sublicensing, or redistribution allowed without written consent.

Links and references

Direct References:

Accelflare.com

WO2022005409A1 - A method and apparatus for hardware accelerated data parsing, processing and enrichment - Patent