Architecture

Core Platform Architecture: VerAI’s architecture is a modular, scalable framework designed to leverage the power of BASE, an Ethereum Layer 2 (L2) rollup, optimizing for low-cost, high-throughput transactions while ensuring flexibility and future-proof integration. Each module operates independently, interacting via standardized APIs and protocols, enabling seamless adaptation to advancements in AI and blockchain technology.

Key Architectural Modules:

1. Resource Abstraction Layer (RAL). The RAL standardizes heterogeneous computational resources (GPUs, CPUs) and datasets into uniform compute units and data tokens, simplifying access for Developers across BASE’s decentralized network and eliminating compatibility issues.

2. Blockchain Layer on BASE. VerAI operates on BASE, an Ethereum L2 rollup that delivers scalability with transaction costs reduced by up to 90% and enhanced throughput, ideal for decentralized AI workloads. The blockchain layer manages transactions, tokenomics, and governance via smart contracts, ensuring transparency and security. VerAI implements a hybrid consensus at the application level: Proof-of-Stake (PoS) for DAO governance and Proof-of-Compute (PoC) for validating computational contributions, complementing BASE’s optimistic rollup mechanism.

3. AI Training Engine: The AI Training Engine enables distributed training of AI models by partitioning tasks across BASE’s high-speed network of nodes, minimizing latency and costs. Supporting frameworks like TensorFlow and PyTorch, it ensures high model accuracy and scalability for deep learning applications.

4. Decentralized Marketplace Module: The marketplace enables Contributors to offer compute power and datasets, and Developers to rent them using $VER tokens, VerAI’s native ERC-20 token on BASE. Dynamic pricing ensures efficient allocation:

$$P_r = P_{\text{base}} \times \left( 1 + \frac{D}{S} \right)$$

Where:

$P_r$ : Price of resource ( r ).

$P_{\text{base}}$ : Base price per unit.

$D$ : Demand for the resource.

$S$ : Supply of the resource.

$VER’s permit functionality (EIP-2612) allows gas-efficient approvals, reducing transaction costs on BASE.

Blockchain Layer: Smart Contracts and Consensus Smart Contracts on BASE Smart contracts automate resource allocation, reward distribution, and governance. The $VER token contract, deployed at 0xea918b73145d32861ae2cfabf4abe3675415624c on BASE, is an ERC-20 token with permit, mint, and burn capabilities, enabling efficient payments and incentives:

pragma solidity ^0.8.0;

contract VERToken {
    string public name = "VerAI";
    string public symbol = "VER";
    uint8 public decimals = 18;
    mapping(address => uint256) public balanceOf;

    function permit(
        address owner,
        address spender,
        uint256 value,
        uint256 deadline,
        uint8 v,
        bytes32 r,
        bytes32 s
    ) external;
    function mint(address to, uint256 amount) external;
    function burn(uint256 amount) external;
}

Additional contracts manage resources and metadata, such as:

pragma solidity ^0.8.0;

contract ResourceAllocation {
    struct Resource {
        address provider;
        uint256 capacity;
        uint256 price;
        bool isAvailable;
    }
    mapping(uint256 => Resource) public resources;
    event ResourceAllocated(uint256 resourceId, address user, uint256 price);

    function allocateResource(uint256 resourceId, address user) external payable {
        require(resources[resourceId].isAvailable, "Resource not available");
        require(msg.value >= resources[resourceId].price, "Insufficient payment");
        resources[resourceId].isAvailable = false;
        payable(resources[resourceId].provider).transfer(msg.value);
        emit ResourceAllocated(resourceId, user, msg.value);
    }
}

Consensus Mechanisms: VerAI leverages BASE’s optimistic rollup for transaction finality, relying on Ethereum L1’s PoS consensus for security. At the application level, VerAI implement

• Proof-of-Stake (PoS): For DAO governance, allowing $VER holders to vote on platform upgrades.

• Proof-of-Compute (PoC): Validates computational work with cryptographic proofs:

$$P = H(T || \text{nonce})$$

Where:

: Proof submitted by the node.

$(H)$ : Cryptographic hash function (SHA-256).

$(T)$ : Computational task.

${nonce}$ : Unique random value.

Decentralized Resource Management Protocol (DRMP) The DRMP ensures efficient resource allocation on BASE:

• Load Balancing: Distributes tasks to prevent node overload:

$$|L_i - L_{\text{avg}}| \leq \tau$$

Where:

$L_i$ : Load on node ( i ).

$L_{\text{avg}}$ : Average network load.

$tau$ : Threshold for load deviation.

• Resource Matching Algorithm: Matches requests with resources:

function matchResources(requests, resources):
    sort resources by cost ascending
    allocation = {}
    for req in requests:
        for res in resources:
            if res.capacity >= req.demand and res.isAvailable:
                allocation[req.id] = res.id
                res.capacity -= req.demand
                res.isAvailable = false if res.capacity == 0
                break
    return allocation

• Fraud Prevention: PoC and BASE’s fraud proofs ensure integrity.

Storage Solutions: IPFS and On-Chain Metadata

IPFS for Dataset Storage

Datasets are stored off-chain on IPFS:

$$\text{CID} = H(\text{dataset})$$

Where: $(H)$ : is a cryptographic hash function (SHA-256).

On-Chain Metadata on BASE

Metadata (ownership, size, permissions) is stored on BASE:

pragma solidity ^0.8.0;

contract DatasetMetadata {
    struct Dataset {
        address owner;
        string cid;
        uint256 size;
        bool isPublic;
        uint256 price;
    }
    mapping(uint256 => Dataset) public datasets;

    function addDataset(uint256 id, string memory cid, uint256 size, bool isPublic, uint256 price) external {
        datasets[id] = Dataset(msg.sender, cid, size, isPublic, price);
    }
}

Networking Protocols for Distributed AI Training

Task Partitioning

Models are split into sub-tasks:

$$M = \bigcup_{i=1}^P M_i$$

Where:

$(M)$ : Full model.

$M_i$ : Sub-task on node ( i ).

$(P)$ : Number of partitions.

Gradient Aggregation

Nodes synchronize updates using AllReduce:

$$\theta_{t+1} = \theta_t - \alpha \nabla L(\theta_t)$$

Where:

$theta_t$ : Parameters at iteration ( t ).

\alpha \ : Learning rate.

\nabla L(\theta_t) \ : Gradient of the loss function.

Fault Tolerance

• Checkpoints: Saved to IPFS, with CIDs on BASE.

• Redundancy: Tasks reassigned on node failure, leveraging BASE’s high throughput.