NPOW: A Neural Proof-of-Work

1. Introduction

3. Implementation Proposal

Layer 1 – 32 kernels — 128 bytes
Layer 2 – 16 kernels — 64 bytes
Layer 3 – 8 kernels — 32 bytes
Layer 4 – Global Average Pooling
Layer 5 – Sigmoid output

3. Conclusion

Drawbacks.

  1. Inability to identify malicious actors submitting multiple proofs.
  2. Inability to ignore duplicate blocks with different proofs.
  3. Higher network bandwidth and compute time for verification of the winning submission.
  4. Unbound-able list of entrants exposes denial of service vector.
  5. Incentive to suppress propagation of other nodes proof submissions.

Benefits.

  1. Less power and resources are used to submit proofs, computers don’t work endlessly to find a solution to a nonce.
  2. How proofs are generated is optional, as the training of the submitted weights is not bound by some constant algorithm, block producers can train weights using any method desired.

4. Solution

5. Implementation Proposal #2

6. Summary

7. Proof-of-Concept

8. White Paper

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