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.

翻译：深度学习的快速普及正日益受到其对高性能硬件（尤其是图形处理器，GPU）严重依赖的制约。这些专用加速器不仅成本高昂、能耗巨大，而且面临严重的供应短缺问题，限制了人工智能在边缘设备上广泛部署的普遍性。这种低效性的核心源于标准人工感知器对密集矩阵乘法的依赖。然而，生物神经系统无需此类算术强度即可实现无与伦比的效率；突触信号传输受物理离子通道极限和化学神经递质水平调控，而非类似于算术乘法的过程。受此生物机制启发，我们提出神经通道网络（NCN），一种旨在将人工智能从昂贵硬件依赖中解耦的新型无乘法架构。在我们的模型中，权重被替换为物理限制信号幅度的通道宽度，而辅助参数则作为神经递质基于符号逻辑调控信号传输。前向传播完全依赖于加法、减法和位运算（最小值、符号），彻底消除了浮点数乘法。在这项概念验证研究中，我们证明NCN能够使用标准反向传播以100%的准确率解决异或和多数函数等非线性可分问题，证实其无需乘法权重即可形成复杂决策边界的能力。该架构为下一代神经形态硬件提供了高效替代方案，为在商用CPU或超低功耗芯片上运行复杂模型而不依赖昂贵GPU集群开辟了道路。