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

TL;DR

This work targets the multiplication bottleneck in deep learning by proposing Neuro-Channel Networks (NCN), a multiplication-free architecture inspired by biological synapses. NCN replaces conventional weights with Channel Widths that clamp signal magnitude and introduces a learnable Neurotransmitter bypass to preserve gradient flow, enabling forward passes that rely only on addition, subtraction, and bitwise logic. The Neuro-Channel Perceptron combines these two paths with a soma-level scaling to stabilize variance, achieving $O(1)$ somatic complexity while eliminating $O(d)$ synaptic multiplications. The authors demonstrate the approach on XOR and Majority functions, achieving 100% accuracy with standard backpropagation, suggesting substantial energy savings and viability for edge and neuromorphic hardware. They outline future directions toward fully multiplication-free training, deeper architectures, and hardware co-design for FPGA/ASIC implementations, positioning NCN as a scalable pathway for efficient, biologically plausible AI.

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.

Figures (4)

• Figure 1: Structure of a single Neuro-Channel Perceptron. The input signal $x_i$ is processed through two parallel paths: a Physical Channel path constrained by the weight width $|w|$, and a Chemical Bypass path gated by the neurotransmitter level $|n|$. Both paths preserve the original sign of the input.
• Figure 2: Architecture of the NCN used for the XOR problem (2-4-2 topology).
• Figure 3: Decision boundary learned by NCN on the XOR function via standard backpropagation. The model successfully separates the non-linear classes using only additive operations.
• Figure 4: Architecture of the NCN used for the 3-bit Majority function (3-8-2 topology).