Neuro-Photonix: Enabling Near-Sensor Neuro-Symbolic AI Computing on Silicon Photonics Substrate

TL;DR

Neuro-Photonix is a novel silicon photonics-based accelerator enabling efficient near-sensor neuro-symbolic AI processing for vision in IoT devices, significantly reducing energy and latency while maintaining accuracy.

Contribution

This work introduces the first near-sensor neuro-symbolic AI accelerator using silicon photonics, integrating analog neural computations with innovative low-cost ADCs and HD vector generation.

Findings

Achieves 30 GOPS/W energy efficiency

Reduces power consumption by 20.8 times compared to ASICs

Maintains accuracy while significantly lowering latency

Abstract

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.