<< Lake

Technical Specification: DeepEven v1.0

Subject: System Parity Analysis via High-Dimensional Weight Matrices // Confidential_Core_Docs

I. ABSTRACT // THEORY_OF_OPERATION

System DeepEven is a hardware-abstracted parity detector. It replaces traditional deterministic CPU logic (modulo(x, 2)) with stochastic, feed-forward matrix algebra. By tokenizing inputs into discrete 16-bit tensors, a single Perceptron calculates the mathematical probability of 'Oddness'.

Note: Operational energy cost per bit exceeds deterministic methods by 10^7. System is explicitly designed for resource utilization testing.

II. COMPLEX SCHEMATIC // NEURAL_GRID_v1

Fig 2.1: Matrix Flow Diagram

Data flow from 16-bit Input Tensor through Weight Matrix ($W$), Bias Shift ($B$), and Sigmoid Rectification.

LSB [X15] X0 X14 ... Σ MATRIX_JCT W15=+20.0 W0=0.0 W14=0.0 Bias: -10.0 [Z] Sigmoid P(Odd)

III. OPERATIONAL_INTERFACE // LIVE_INFERENCE

Enter integer value for parity analysis. System will tokenize and execute forward-propagation on remote PyTorch infrastructure.

IV. TECHNICAL_SPECIFICATIONS

  • Core Logic: Single-Layer Perceptron (SLP) Feed-Forward Network.
  • Tokenization: Integer $\rightarrow$ 16-bit normalized floating-point tensor ($$ \in \{0.0, 1.0\} $$).
  • Weight Priming: Manual $W_{15}$ amplification ($$+20.0$$); $W_{0\dots 14}$ suppression.
  • Bias Correction: Static manual shift ($$-10.0$$) to establish decision threshold.
  • Activation Function: Non-Linear Sigmoid rectification.