Deep neural networks have achieved considerable success over the past ten years in a variety of fields. However, current state-of-the-art artificial intelligence (AI) systems require large computing hardware infrastructure and high power consumption. To overcome these hurdles, it is required to adopt new strategies such as designing novel computation architectures and developing building blocks that can mimic the low energy consumption of biological systems. On the architecture level, implementing classification tasks by splitting the problem into simpler subtasks is a way to relax hardware constraints despite the less accuracy of the approach. On the computation unit level, memristive devices are a promising technology for low power neuromorphic computation. Hereby, we combine both these two approaches and present a novel algorithmic approach for multiclass classification tasks through splitting the problem into binary subtasks while using optoelectronics memristors as synapses. Our approach leverages the core fundamentals from the One-vs-One (OvO) and the One-vs-Rest (OvR) classification strategies towards a novel Outcome-Driven One-vs-One (ODOvO) approach. The light modulation of synaptic weights, fed in our algorithm from experimental data, is a key enabling parameter that permits classification without modifying further applied electrical biases. Our approach requires at least a 10X less synapses (only 196 synapses are required) while reduces the classification time by up to \(\frac{N}{2}\) compared to conventional memristors. We show that the novel ODOvO algorithm has similar accuracies to OvO (reaching over 60% on the MNIST dataset) while requiring even fewer iterations compared to the OvR. Consequently, our approach constitutes a feasible solution for neural networks where key priorities are the minimum energy consumption i.e., small iterations number, fast execution, and the low hardware requirements allowing experimental verification.

过去十年来，深度神经网络在各个领域取得了相当大的成功。然而，当前最先进的人工智能（AI）系统需要大型计算硬件基础设施和高功耗。为了克服这些障碍，需要采用新的策略，例如设计新颖的计算架构和开发可以模仿生物系统低能耗的构建模块。在架构层面上，通过将问题拆分为更简单的子任务来实现分类任务是一种放松硬件限制的方法，尽管该方法的准确性较低。在计算单元层面，忆阻器件是一种有前途的低功耗神经形态计算技术。因此，我们结合这两种方法，通过将问题分解为二进制子任务，同时使用光电忆阻器作为突触，提出了一种用于多类分类任务的新颖算法方法。我们的方法利用一对一（OvO）和一对一（OvR）分类策略的核心基础知识来实现​​一种新颖的结果驱动一对一（ODOvO）方法。根据实验数据输入我们的算法的突触权重的光调制是一个关键的启用参数，它允许在不修改进一步施加的电偏置的情况下进行分类。与传统忆阻器相比，我们的方法至少需要减少 10 倍的突触（仅需要 196 个突触），同时将分类时间减少多达\(\frac{N}{2}\) 。我们表明，新颖的 ODOvO 算法具有与 OvO 相似的精度（在 MNIST 数据集上达到 60% 以上），而与 OvR 相比，需要的迭代次数甚至更少。因此，我们的方法构成了神经网络的可行解决方案，其中关键优先级是最小能耗，即迭代次数少、执行速度快以及允许实验验证的硬件要求低。