This initial phase focuses on solidifying Blumind's analog AI as the primary novel computing paradigm for investigation, aligning with its production-readiness and our team's established access. We will conduct a deep dive into Hyperon's architecture (PLN, ECAN) to identify and define 2-3 specific, high-impact AGI time-series tasks where analog AI's ultra-low power and real-time capabilities could offer significant advantages (e.g., complex precursor detection, nuanced attention cue processing from sensor streams). A detailed research plan will be formulated, outlining the precise methodology for subsequent benchmarking against conventional digital AI accelerators (e.g., ARM Cortex-M MCUs). This includes defining baseline hardware, software libraries (CMSIS-NN, TFLite Micro), relevant datasets (either existing or to-be-generated with clear specifications), and key performance indicators. We will also initiate formal procedures to secure Blumind development kits and any necessary proprietary technical documentation, leveraging our existing relationship via Raymond Chik to ensure timely access. The outcome is a robust framework for the entire project.

1. **Detailed Research Plan Document (PDF):** * Confirmation and rationale for selecting Blumind's analog AI. * Comprehensive descriptions of 2-3 selected AGI-specific time-series tasks, including their relevance to Hyperon components. * Detailed benchmarking methodology: * Specification of baseline digital hardware and software for comparison. * Approach for dataset selection/creation and preprocessing. * Defined quantitative and qualitative metrics for evaluation. * Project timeline and risk mitigation strategies for subsequent phases. * Initial assessment of Blumind's technology's potential relevance to the selected AGI workloads. 2. **Blumind Development Kit Acquisition & Setup Report:** Document confirming the status of acquiring Blumind development kits and establishing the initial development environment.

$10,000 USD

1. SingularityNET's formal approval of the submitted Detailed Research Plan, confirming alignment with RFP objectives and methodological soundness. 2. Clear justification and documentation for the selected 2-3 AGI-specific time-series tasks, demonstrating their potential impact and suitability for analog AI exploration. 3. Benchmarking methodology, including baselines, datasets, and metrics, is well-defined, robust, and agreed upon. 4. Tangible progress on securing Blumind development kits and necessary technical documentation, with a clear path to full development environment setup by the start of Milestone 2. Any foreseen delays must be communicated with a mitigation plan.

This phase centers on academic rigor and precise experimental design. We will conduct a focused literature review on analog computing applications relevant to AGI, specifically investigating existing research on processing temporal data, potential pathways for recursive reasoning precursors, and energy-efficient attention mechanisms. This review will draw from peer-reviewed sources and expert consultations where appropriate. Building on Milestone 1, we will finalize and meticulously document the benchmarking criteria. This includes specifying exact data formats, preprocessing steps for both analog and digital platforms, and precise mathematical definitions for all performance metrics (e.g., energy consumption per inference in nanoJoules, inference latency from input to output in milliseconds, F1-scores or AUC-ROC for classification tasks, specific error rates for precursor detection). A crucial activity will be the preparation and validation of test datasets for the selected AGI tasks, ensuring they are representative, of sufficient size for meaningful comparison, and compatible with both Blumind's hardware and the chosen baseline digital platforms.

1. **Focused Literature Review Report (PDF):** A comprehensive review of existing research on analog computing for AGI-relevant tasks, emphasizing temporal data processing, potential links to recursive reasoning components, and attention mechanisms. Includes bibliography of peer-reviewed sources. 2. **Finalized Benchmarking Criteria Document (PDF):** Detailed specifications for all performance metrics, data formats, and experimental protocols. Clearly defines how Blumind’s architecture will be compared against traditional digital architectures. 3. **Prepared Test Datasets & Documentation:** The actual test datasets (or clear instructions/scripts to generate them) for the selected AGI tasks, along with documentation describing their sources, characteristics, preprocessing steps, and suitability for the benchmarking process.

$10,000 USD

1. Submission of a comprehensive and well-researched literature review that grounds the project in existing scientific knowledge and clearly identifies gaps or opportunities for the proposed work. 2. SingularityNET's approval of the Finalized Benchmarking Criteria Document, ensuring the planned comparisons will be fair, rigorous, and informative. 3. Test datasets are fully prepared, documented, validated for quality and relevance, and ready for use in Milestone 3 experiments on both Blumind and baseline digital platforms. 4. All development environment prerequisites, including Blumind SDKs and toolchains, are fully operational.

This phase transitions to hands-on experimental validation. The primary focus will be the implementation and porting of the AGI-relevant time-series algorithms (defined in Milestone 1) onto Blumind's analog AI development kits. This will involve firmware development, algorithm adaptation to suit the analog architecture, and extensive debugging, leveraging our team's hardware engineering capabilities and Blumind's technical support. We will conduct initial experimental runs for each AGI task on the Blumind hardware, collecting preliminary data on power consumption, inference latency, and task-specific accuracy. Simultaneously, we will set up and run the same tasks on the baseline digital AI accelerators. This phase also involves active software-hardware co-design exploration, identifying any architectural nuances or data pipeline optimizations beneficial for the analog platform. All findings, including challenges encountered during porting or execution, and any necessary model refinements or algorithmic adjustments, will be meticulously documented.

1. **Interim Experimental Report (PDF):** * Presentation of early experimental results: preliminary figures for power consumption, latency, and accuracy for each AGI task on Blumind's platform and, if available, initial comparative data from digital baselines. * Detailed account of the porting process, including challenges faced and solutions implemented. * Notes on software-hardware co-design considerations specific to the Blumind architecture. 2. **Developed Firmware/Software Package (e.g., Git repository snapshot):** Source code for the algorithms implemented on the Blumind platform, along with any test scripts or utilities developed. 3. **Updated Documentation:** Refined algorithm descriptions based on implementation experience, revised test plans based on early findings.

$10,000 USD

1. Successful porting and execution of at least two AGI-relevant algorithms on Blumind's analog AI hardware, demonstrating functional operation. 2. Collection of initial, verifiable performance data (power, latency, accuracy) from the Blumind platform for the implemented tasks. 3. Clear documentation of the development process, including any firmware/software produced and a candid assessment of challenges and lessons learned. 4. Evidence of engagement with software-hardware co-design principles during implementation. 5. Comparative experiments on baseline digital platforms initiated and yielding preliminary data.

This final phase involves the completion of all benchmarking experiments and the comprehensive analysis of results. We will synthesize the data collected on Blumind's analog AI and the baseline digital platforms to conduct a thorough comparative evaluation across all defined metrics. This analysis will form the basis of a feasibility assessment regarding the scalability, energy efficiency, and long-term viability of integrating such analog co-processors into Hyperon or similar AGI architectures. Practical integration pathways and potential software-hardware co-design implications for larger AGI systems will be discussed. A key activity is the development and presentation of a polished proof-of-concept (PoC) demonstration, showcasing one of the successfully implemented AGI tasks running on Blumind's hardware, highlighting its performance advantages. The project culminates in a final, comprehensive report detailing all research activities, findings, analyses, and recommendations.

1. **Final Comprehensive Report (PDF):** * Complete comparative analysis of Blumind's analog AI vs. traditional digital architectures for all tested AGI tasks, supported by quantitative data and statistical analysis. * In-depth feasibility assessment addressing scalability, energy efficiency, integration potential, and cost-benefit for AGI applications. * Discussion of software-hardware co-design learnings and recommendations for future AGI hardware development. * Overall conclusions and recommendations to SingularityNET. 2. **Proof-of-Concept (PoC) Demonstration Package:** * Video recording of the PoC showcasing the Blumind chip executing a selected AGI-relevant time-series task, demonstrating its functionality and performance. * All source code (firmware, test scripts, analysis scripts) and necessary documentation to understand and potentially replicate the PoC. * The physical PoC setup (if feasible and requested by SingularityNET for inspection). 3. **Complete Project Archive:** All developed software, datasets (or generators), research notes, presentations, and reports.

$10,000 USD

1. Submission of a high-quality, comprehensive Final Report that clearly presents all findings, provides a robust comparative analysis, and offers actionable insights and recommendations to SingularityNET. 2. Successful demonstration of a compelling Proof-of-Concept where Blumind's analog AI chip performs a defined AGI-relevant time-series task, clearly showcasing the evaluated benefits (e.g., significantly lower power, acceptable/good accuracy). 3. The PoC demonstration and its accompanying materials are clear, well-documented, and effectively communicate the project's achievements. 4. All project deliverables (reports, code, data, PoC) are submitted, meet the RFP requirements, and are formally accepted by SingularityNET. 5. The project clearly articulates the potential of the selected alternative computing paradigm for AGI, fulfilling the RFP's main objective.