{"slug": "absl-100-integer-only-neural-network-training-in-rust", "title": "ABSL – 100% Integer-Only Neural Network Training in Rust", "summary": "A 15-year-old developer in Germany has released ABSL (Adaptive Bit-Shift Learning) v1.0.0, a 100% integer-only neural network training algorithm written in Rust that achieves 91.1% global accuracy on the XOR problem by eliminating floating-point math and using adaptive bit-shifting for weight updates. The algorithm, which runs on ultra-low-power embedded systems without FPUs, improves perfect-run rate from 38.7% in v3 to 70.1% in v5 and is being tuned toward 99%+ accuracy and MNIST scaling.", "body_md": "ABSL (Adaptive Bit-Shift Learning) is an experimental, **100% integer-only learning algorithm** for neural networks, written from scratch in Rust.\n\nBy completely avoiding floating-point math (`float`\n\n), 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.\n\nInstead of traditional learning rates, ABSL dynamically scales weight updates using an adaptive bit-shifting mechanism based on integer error magnitudes.\n\nHistorically, 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.\n\nBy 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.\n\n| Metric | ABSL v3 | ABSL v5 (v1.0.0 Core) |\n|---|---|---|\nPerfect Runs (4/4 Correct) |\n38.7 % | 70.1 % 🔥 |\nGlobal Accuracy |\n80.0 % | 91.1 % |\nAvg. Correct Cases per Run |\n3.20 / 4 | 3.64 / 4 |\nTotal Failures (0, 1, or 2 Correct) |\nHigh | 0.0 % (Always gets ≥ 3/4) |\n\n**(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)*\n\nABSL snaps continuous gradients into a discrete integer grid using native Rust performance:\n\n```\nimpl LearningRule for ABSLv5 {\n    fn update(&self, error: i32, input: i32, weight: i32, _epoch: u32) -> i32 {\n        let shift = Self::calculate_shift(error, weight);\n        (((error as i64) * (input as i64)) >> shift) as i32\n    }\n}\n```\n\nThe `shift`\n\nis dynamically calculated using bit-lengths (`ilog2`\n\n) of both the error and weight magnitudes, preventing weight explosion while keeping execution blindingly fast.\n\nI am actively working on pushing the success rate from **70.1% to 99%+.**\n\n- Implement a discrete integer damping factor (Momentum equivalent) to prevent weight oscillation.\n- Scale to MNIST.\n\n##About Me:\n\n-\nI'm 15 living in Germany cirrntly in 10th grade and coded al of this on a broken s22 Ultra\n\n-\nIn the future i want to study and work even more with this topic\n\nCostum Licene please look in LICENSE", "url": "https://wpnews.pro/news/absl-100-integer-only-neural-network-training-in-rust", "canonical_source": "https://github.com/Mojo0869/ABSL", "published_at": "2026-07-30 13:34:24+00:00", "updated_at": "2026-07-30 13:52:39.610639+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "neural-networks", "ai-research", "developer-tools"], "entities": ["ABSL", "Adaptive Bit-Shift Learning", "Rust", "XOR problem", "MNIST"], "alternates": {"html": "https://wpnews.pro/news/absl-100-integer-only-neural-network-training-in-rust", "markdown": "https://wpnews.pro/news/absl-100-integer-only-neural-network-training-in-rust.md", "text": "https://wpnews.pro/news/absl-100-integer-only-neural-network-training-in-rust.txt", "jsonld": "https://wpnews.pro/news/absl-100-integer-only-neural-network-training-in-rust.jsonld"}}