🤖 AI Summary
This work addresses the high computational cost and energy consumption of deep learning—stemming from its reliance on massive floating-point multiplications—which hinders deployment on edge devices. Inspired by biological neural systems, the authors propose a novel multiplication-free neural network architecture that restricts signal amplitude through channel width and introduces trainable “neurotransmitter” parameters to modulate signal transmission under symbolic logic. Forward propagation is achieved using only addition, subtraction, and bitwise operations. By translating the physical saturation mechanism of biological synapses into a trainable, multiplication-free structure, the method achieves 100% accuracy on nonlinear tasks such as XOR and majority functions, demonstrating its capacity to model complex decision boundaries. This approach establishes a new paradigm for low-power, GPU-independent AI deployment.
📝 Abstract
The rapid proliferation of Deep Learning is increasingly constrained by its heavy reliance on high-performance hardware, particularly Graphics Processing Units (GPUs). These specialized accelerators are not only prohibitively expensive and energy-intensive but also suffer from significant supply scarcity, limiting the ubiquity of Artificial Intelligence (AI) deployment on edge devices. The core of this inefficiency stems from the standard artificial perceptron's dependence on intensive matrix multiplications. However, biological nervous systems achieve unparalleled efficiency without such arithmetic intensity; synaptic signal transmission is regulated by physical ion channel limits and chemical neurotransmitter levels rather than a process that can be analogous to arithmetic multiplication. Inspired by this biological mechanism, we propose Neuro-Channel Networks (NCN), a novel multiplication-free architecture designed to decouple AI from expensive hardware dependencies. In our model, weights are replaced with Channel Widths that physically limit the signal magnitude, while a secondary parameter acts as a Neurotransmitter to regulate Signal Transmission based on sign logic. The forward pass relies exclusively on addition, subtraction, and bitwise operations (minimum, sign), eliminating floating-point multiplication entirely. In this proof-of-concept study, we demonstrate that NCNs can solve non-linearly separable problems like XOR and the Majority function with 100% accuracy using standard backpropagation, proving their capability to form complex decision boundaries without multiplicative weights. This architecture offers a highly efficient alternative for next-generation neuromorphic hardware, paving the way for running complex models on commodity CPUs or ultra-low-power chips without relying on costly GPU clusters.