GitHub - Mojo0869/ABSL: ABSL or Adaptive Bitshift learning is a lerning method only using Integers for AI i made. The goal is to make a good Integer Neuronr neuron · GitHub
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Mojo0869
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ABSL – Adaptive Bit-Shift Learning (v1.0.0 XOR Edition)
ABSL (Adaptive Bit-Shift Learning) is an experimental, 100% integer-only learning algorithm for neural networks, written from scratch in Rust.
By completely avoiding floating-point math (float), ABSL eliminates the need for expensive FPUs (Floating Point Units). This makes it natively compatible with ultra-low-power embedded systems, 8-bit/16-bit microcontrollers, and neuromorphic hardware.
Instead of traditional learning rates, ABSL dynamically scales weight updates using an adaptive bit-shifting mechanism based on integer error magnitudes.
🚀 The Breakthrough: Cracking the Non-Linear XOR Problem
Historically, solving the non-linear XOR problem required continuous floating-point gradients. ABSL v1.0.0 completely shatters the myth that integer training is bound to get stuck in local minima due to harsh rounding errors.
By scaling up the hidden layer to a 2-3-1 architecture (2 Inputs, 3 Hidden Neurons, 1 Output), ABSL introduces enough high-dimensional redundancy to bypass integer quantization bottlenecks.
Latest Benchmark Results (1,000 Runs)
Metric<br>ABSL v3<br>ABSL v5 (v1.0.0 Core)
Perfect Runs (4/4 Correct)<br>38.7 %<br>70.1 % 🔥
Global Accuracy<br>80.0 %<br>91.1 %
Avg. Correct Cases per Run<br>3.20 / 4<br>3.64 / 4
Total Failures (0, 1, or 2 Correct)<br>High<br>0.0 % (Always gets ≥ 3/4)
Input Combination Accuracy (v1.0.0)
(0,0): 88.2% (Current tuning target: eliminating low-level integer bias leakage)
(0,1): 99.4% ✨
(1,0): 99.6% ✨
(1,1): 77.1% (Current tuning target: optimizing negative integer inhibition)
🧠 How It Works (The Core Logic)
ABSL snaps continuous gradients into a discrete integer grid using native Rust performance:
i32 {<br>let shift = Self::calculate_shift(error, weight);<br>(((error as i64) * (input as i64)) >> shift) as i32<br>}">impl LearningRule for ABSLv5 {<br>fn update(&self, error: i32, input: i32, weight: i32, _epoch: u32) -> i32 {<br>let shift = Self::calculate_shift(error, weight);<br>(((error as i64) * (input as i64)) >> shift) as i32
The shift is dynamically calculated using bit-lengths (ilog2) of both the error and weight magnitudes, preventing weight explosion while keeping execution blindingly fast.
🎯 Current Roadmap & Optimization Targets
I am actively working on pushing the success rate from 70.1% to 99%+.
Next Steps:
Implement a discrete integer damping factor (Momentum equivalent) to prevent weight oscillation.
Scale to MNIST.
##About Me:
I'm 15 living in Germany cirrntly in 10th grade and coded al of this on a broken s22 Ultra
In the future i want to study and work even more with this topic
License
Costum Licene please look in LICENSE
About<br>ABSL or Adaptive Bitshift learning is a lerning method only using Integers for AI i made. The goal is to make a good Integer Neuronr neuron<br>Resources<br>Readme<br>License<br>Activity<br>Stars<br>2 stars<br>Watchers<br>0 watching<br>Forks<br>0 forks<br>Report repository
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