Accelflare

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Parallel Computing Paradigms for Next-Gen AGI

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Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Explore novel hardware architectures and computing paradigms for AGI

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Overview

This project explores next-generation computing paradigms—such as analog, temporal, and in-memory architectures—for accelerating symbolic AGI systems. Through structured evaluation and benchmarking, we aim to identify architectures capable of supporting parallel symbolic workloads in reasoning, inference, and attention mechanisms. Using our Symbolic AGI Toolkit and CQ Tester, we will assess feasibility, performance, and integration potential. Outcomes will include research insights, benchmark criteria, and a prototype demonstration aligned with AGI development goals.

RFP Guidelines

Explore novel hardware architectures and computing paradigms for AGI

Proposal Submission (7 days left)

Type SingularityNET RFP
Total RFP Funding $80,000 USD
Proposals 3
Awarded Projects n/a

SingularityNET

Apr. 14, 2025

SingularityNET

rfp owner

Explore novel hardware architectures and computing paradigms for AGI

Proposal Submission (7 days left)

The purpose of this RFP is to identify, assess, and experiment with novel computing paradigms that could enhance AGI system performance and efficiency. By focusing on alternative architectures, this research aims to overcome computational bottlenecks in recursive reasoning, probabilistic inference, attention allocation, and large-scale knowledge representation. Bids are expected to range from $40,000 - $80,000.

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Proposal Description

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Total Milestones
4
Total Budget
$40,000_USD
Last Updated
13 May 2025

Milestone 1 - Paradigm Selection & Research Design

Description

Hardware Paradigm Mapping & AGI Task Alignment: Survey and select candidate hardware paradigms (e.g. analog in-memory temporal race logic). Design a research strategy that maps AGI symbolic workloads—including recursive reasoning FSM chains and Regex interpretation—onto these paradigms.

Deliverables

Detailed research plan Symbolic task catalog and alignment matrix Diagram of AGI task flow mapped to each hardware paradigm

Budget

$10,000 USD

Success Criterion

Comprehensive justification for each paradigm’s relevance Clearly defined benchmarking methodology Approval-ready roadmap for experimentation

Milestone 2 - Literature Review & Benchmark Criteria

Description

Symbolic Hardware Suitability & Benchmarking Metrics: Conduct an in-depth literature review of the selected paradigms focused on symbolic execution attention shifts and recursive inference. Define benchmark metrics for evaluating symbolic processing across AGI workloads.

Deliverables

Annotated literature summary Benchmarking matrix (performance scalability symbolic traceability) Symbolic task simulation scenarios

Budget

$10,000 USD

Success Criterion

At least 4 paradigms deeply analyzed Formal benchmark definitions for FSM/Regex/CQ tasks Validation-ready testing criteria for Milestone 3

Milestone 3 - Simulation & Feasibility Testing

Description

Symbolic Execution Simulations and Feasibility Trials: Build simulation environments or use hardware-software co-design tools to evaluate AGI-relevant tasks under each paradigm. Focus on task fidelity performance efficiency and symbolic integrity.

Deliverables

FSM/Regex/CQ test execution logs Symbolic task graphs before/after execution Performance and symbolic consistency reports

Budget

$10,000 USD

Success Criterion

Successful symbolic task simulation on at least two paradigms Feasibility ratings based on benchmark metrics Evidence of symbolic traceability through execution

Milestone 4 - Evaluation & Prototype Demonstration

Description

Comparative Review and AGI Integration Prototype: Synthesize findings into a comparative evaluation report. Build a prototype or symbolic proof-of-concept demonstrating how one selected paradigm can accelerate symbolic AGI tasks.

Deliverables

Final evaluation report with symbolic integration mapping Working prototype or co-simulation model Summary recommendations for future AGI hardware research

Budget

$10,000 USD

Success Criterion

Prototype demonstrates successful symbolic execution Paradigm shown to improve AGI task throughput or efficiency Deliverables suitable for future AGI infrastructure proposals

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The weighted average of the 4 perspectives Overall

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Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

0.0
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

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Review Headline

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding. Viability (secondary) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary) Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference? Example:

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

Usefulness (secondary) This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?” For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?). For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things? Examples:

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

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