Reverse Engineering a Neural Network's Clever Solution to Binary Addition (2023)
19 days ago
- #binary-addition
- #neural-networks
- #machine-learning
- Small neural networks (<1000 parameters) can be surprisingly effective for specialized tasks.
- A neural network was trained to perform 8-bit binary addition, including handling overflow.
- The network successfully learned the task with as few as 422 parameters across 3 layers.
- The network used a custom activation function (Ameo) in the first layer and tanh in others.
- The network's solution resembled a digital-to-analog converter (DAC), converting binary inputs to analog signals.
- Neurons in the first layer generated sine wave-like outputs with periods matching binary digit switches.
- Later layers combined and routed these signals, making them more square wave-like.
- The network's approach was unexpected, leveraging analog signal processing rather than digital logic gates.
- The findings suggest that more efficient architectures could reduce the size of large neural networks.
- The experiment highlights the power of gradient descent and optimization algorithms in neural networks.