Neuro-symbolic Artificial Intelligence (AI) models, blending neural networks with symbolic AI, have facilitated transparent reasoning and context understanding without the need for explicit rule-based programming. However, implementing such models in the Internet of Things (IoT) sensor nodes presents hurdles due to computational constraints and intricacies. In this work, for the first time, we propose a near-sensor neuro-symbolic AI computing accelerator named Neuro-Photonix for vision applications. Neuro-photonix processes neural dynamic computations on analog data while inherently supporting granularity-controllable convolution operations through the efficient use of photonic devices. Additionally, the creation of an innovative, low-cost ADC that works seamlessly with photonic technology removes the necessity for costly ADCs. Moreover, Neuro-Photonix facilitates the generation of HyperDimensional (HD) vectors for HD-based symbolic AI computing. This approach allows the proposed design to substantially diminish the energy consumption and latency of conversion, transmission, and processing within the established cloud-centric architecture and recently designed accelerators. Our device-to-architecture results show that Neuro-Photonix achieves 30 GOPS/W and reduces power consumption by a factor of 20.8 and 4.1 on average on neural dynamics compared to ASIC baselines and photonic accelerators while preserving accuracy.

翻译：神经符号人工智能模型融合了神经网络与符号人工智能，无需依赖显式的基于规则的编程，即可实现透明推理与上下文理解。然而，在物联网传感器节点中部署此类模型，由于计算资源受限与系统复杂性，仍面临诸多挑战。本工作首次提出一种面向视觉应用的近传感器神经符号人工智能计算加速器，命名为Neuro-Photonix。Neuro-Photonix在模拟数据上执行神经动态计算，同时通过高效利用光子器件，固有地支持粒度可控的卷积运算。此外，我们设计了一种创新的低成本模数转换器，可与光子技术无缝协同工作，从而消除了对昂贵模数转换器的需求。更重要的是，Neuro-Photonix能够生成用于基于超维度的符号人工智能计算的超维度向量。该方法使得所提出的设计能够显著降低在以云为中心的现有架构及近期设计的加速器中，转换、传输与处理过程中的能耗与延迟。我们的器件至架构级实验结果表明，Neuro-Photonix实现了30 GOPS/W的能效，在神经动态计算方面，相较于专用集成电路基准方案与光子加速器，平均功耗分别降低了20.8倍与4.1倍，同时保持了计算精度。