Neuro-Photonix: Enabling Near-Sensor Neuro-Symbolic AI Computing on Silicon Photonics Substrate
Deniz Najafi, Hamza Errahmouni Barkam, Mehrdad Morsali, SungHeon, Jeong, Tamoghno Das, Arman Roohi, Mahdi Nikdast, Mohsen Imani, Shaahin Angizi

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…
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