Neuro-Channel Networks: A Multiplication-Free Architecture by Biological Signal Transmission

TL;DR

Neuro-Channel Networks (NCNs) are a biologically inspired, multiplication-free neural architecture that uses physical channel limits and sign-based regulation to perform complex tasks efficiently without traditional matrix multiplications.

Contribution

This paper introduces NCNs, a novel neural network architecture that eliminates multiplication by mimicking biological signal transmission, enabling efficient computation on low-power hardware.

Findings

Successfully solved XOR and Majority problems with 100% accuracy.

Demonstrated the feasibility of multiplication-free neural computation.

Potential for deployment on low-power, hardware-constrained devices.

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.