There is a need for machine learning models to evolve in unsupervised circumstances. New classifications may be introduced, unexpected faults may occur, or the initial dataset may be small compared to the data-points presented to the system during normal operation. Implementing such a system using neural networks involves significant mathematical complexity, which is a major issue in power-critical edge applications. This paper proposes a novel field-programmable gate-array infrastructure for online learning, implementing a low-complexity machine learning algorithm called the Tsetlin Machine. This infrastructure features a custom-designed architecture for run-time learning management, providing on-chip offline and online learning. Using this architecture, training can be carried out on-demand on the \ac{FPGA} with pre-classified data before inference takes place. Additionally, our architecture provisions online learning, where training can be interleaved with inference during operation. Tsetlin Machine (TM) training naturally descends to an optimum, with training also linked to a threshold hyper-parameter which is used to reduce the probability of issuing feedback as the TM becomes trained further. The proposed architecture is modular, allowing the data input source to be easily changed, whilst inbuilt cross-validation infrastructure allows for reliable and representative results during system testing. We present use cases for online learning using the proposed infrastructure and demonstrate the energy/performance/accuracy trade-offs.

翻译：针对机器学习模型在无监督场景下演进的需求，例如新分类的引入、意外故障的发生，或初始数据集相较于系统正常运行期间呈现的数据点规模过小等情况，本研究提出一种新型现场可编程门阵列基础设施用于在线学习，该方案实现了名为Tsetlin机器的低复杂度机器学习算法。该基础设施采用定制化运行时学习管理架构，支持片内离线学习与在线学习。利用该架构，可在推理前使用预分类数据在FPGA上按需执行训练。此外，该架构还支持在线学习功能，允许在系统运行期间将训练与推理交错进行。Tsetlin机器的训练过程会自然收敛至最优解，同时训练过程与阈值超参数相关联，该参数用于随着Tsetlin机器训练程度的加深而降低反馈生成概率。所提架构具有模块化特性，可便捷更换数据输入源，其内置交叉验证基础设施能在系统测试期间提供可靠且具有代表性的结果。本文通过具体用例展示了基于该基础设施的在线学习实现方案，并分析了能效、性能与精度之间的权衡关系。