Jonathan Edwards

Project Owner

Temporal Computing

Type SingularityNET RFP
Funding Awarded $80,000 USD
RFP Guidelines Explore novel hardware architectures and computing paradigms for AGI

Status

Overall Status
⏳ Contract Pending
Funding Transfered
$0_USD
Max Funding Amount
$80,000_USD

Funding Schedule

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Milestone Release 1	$14,997 USD	Pending	TBD
Milestone Release 2	$15,000 USD	Pending	TBD
Milestone Release 3	$45,003 USD	Pending	TBD
Milestone Release 4	$5,000 USD	Pending	TBD

Overview

Our company, Temporal Computing, proposes to enhance the performance and scalability of the Hyperon framework by integrating cutting-edge temporal computing technologies with specialized hardware. Temporal computing offers an alternative to traditional and quantum computing by representing input data as a "passage of time" rather than traditional binary. This method allows for processing efficiencies and energy savings which can be utilised in both traditional AI compute and graph and crypto fast compute settings. Our vision is to leverage these advantages to provide a significant speed increase and power reduction in AGI computations.

RFP Guidelines

Explore novel hardware architectures and computing paradigms for AGI

Complete & Awarded

Type SingularityNET RFP
Total RFP Funding $80,000 USD
Proposals 9
Awarded Projects 1

SingularityNET

Apr. 14, 2025

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.

Proposal Description

Company Name (if applicable)

Temporal Computing

Project details

Our company, Temporal Computing, proposes to enhance the performance and scalability of the Hyperon framework by integrating cutting-edge temporal computing technologies with specialized hardware. Temporal computing offers an alternative to traditional and quantum computing by representing input data as a "passage of time" rather than traditional binary. This method allows for processing efficiencies and energy savings by utilizing simpler memory manipulation devices. Our vision is to leverage these advantages to provide a significant speed increase (100x) and power reduction (100x) in AGI computations.

Temporal computing has several advantages over quantum computing, including easier development, higher parallelization capacity, and low resource requirements. Key problems identified include deep learning, flow optimization in graphs, and cryptographic systems.

Our approach includes both electrical and non-silicon implementations, with a focus on proving the viability of temporal computing in various domains. The project targets improvements in low power and high-performance compute, aiming to capture a significant share of the neuromorphic computing market and demonstrate "supremacy" in valuable areas like AI systems.

A variety of papers, patents, and reports have documented our work to date, with a focus on developing a licensable portfolio and unlocking future investment. The plan includes milestones for both software and hardware development, aiming to build a general temporal computer that can solve a wide range of problems.

SInce submitting this bid in a previous round the temporal idea has moved on significantly - we have built and "taped-out" a small but useful temporal sequential computer. We are finding that the ideas of sparsity in a unary data format has wroked well and we can typically save around 50x on add and multiply compared to traditional architectures.

Project Objectives:

1.Evaluate MeTTa, MeTTa compilers, and Hyperon technologies to identify opportunities for the integration of temporal technology.

2.Implement this technology and benchmark against known systems. We have a broad agenda which is to initial build small sequential processors, then add the highly desireable "Neural" features, which will then be highly parallelised into a network structure.

3. Start the process of turning this into an open source Neural Processing platform

Scope:

We propose a comprehensive approach that includes:

1. Codebase rethinking and compiler optimization to take advantage of temporal computing insights.

2. Integration of temporal IP into the Hyperon stack.

3. Research and development of POC hardware solutions for key AGI components, focusing on deep learning with the potential to extend to cryptographic systems.

Key Deliverables:

A comprehensive analysis of the current state of art hardware for the respective computational requirements.
Recommendations for integrating temporal hardware into the Hyperon/PRIMUS stack.
Proof-of-concept prototypes and testbeds for key AGI components.

Methodology:

Our approach involves the following stages:

Literature review and analysis of existing works on MeTTa, MeTTa compilers, Hyperon technologies, and temporal computing.
Stakeholder engagement with industry experts to identify priority areas for improvement.
Technical KPI assessments to determine feasibility and scalability.
Research and development of specialized hardware solutions.

Timeline:

Phase 1 (0-1 months): Literature review, stakeholder engagement, and technical KPI assessments.
Phase 2 (1-2 months): Development of temporary computing insights and recommendations for integrating specialized hardware into the Hyperon stack.
Phase 3 (2-6 months): Research and development of specialized hardware solutions and construction of a demonstrator.

Budget:

Our estimated budget for this project is $80000, which will be allocated as follows:

Personnel and overheads: 60000
Equipment and infrastructure: 10000
Research and development: 10000

Conclusion:

Temporal Computing is confident that our approach will significantly enhance the performance and scalability of the Hyperon framework. We believe that our proposed solutions will capture a significant share of the neuromorphic computing market and demonstrate "supremacy" in valuable areas like autonomous systems.

Contact Information:

For more information, please visit our website at https://temporal.computer/ or contact us at Jonny@temporalcomputing.com.

Links and references

temporal.computer

Additional videos

Temporal Computing, the key points | Information Underload

Temporal Computing, new videos | Information Underload

Activity Summary

Milestones

4

Total

Discussion

1

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Project Team

2

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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.

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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.

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

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.

Total Milestones
4
Total Budget
$80,000_USD
Last Updated
10 Sep 2025

Milestone 1 - Milestone 1 Literature Review and Stakeholder

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Rob Freeman
Aug 30, 2025 | 12:49 AM

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<a href="I'm interested in the Temporal Computer idea. It might relate to what I'm doing with" rel="nofollow ugc">I'm interested in the Temporal Computer idea. It might relate to what I'm doing with "phase based" rather than "rate based" neural models. Synchrony, but also recently Izhikevich "polychrony"</a>

I'm interested in the Temporal Computer idea. It might relate to what I'm doing with "phase based" rather than "rate based" neural models. Synchrony, but also recently Izhikevich "polychrony"

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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

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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

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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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The weighted average of the 4 perspectives Overall

0.0
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

0.0

Review Headline

0 /50 chars

0 /5000 chars

Warning: Adding final group rating for this project will prevent expert users from adding new or editing existing reviews

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.

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

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.

Jonathan Edwards

Project Owner

Visonary!, Leader, Programmer, hardware designer

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Design Engineer

Hands-on with the silicon - Sam will manage the tape-outs, and will generally manage delivery of the milestones.

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