Neuro-Photonix: Enabling Near-Sensor Neuro-Symbolic AI Computing on Silicon Photonics Substrate (2412.10187v1)

Abstract: Neuro-symbolic 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.

• The paper introduces Neuro-Photonix, a near-sensor computing architecture consolidating neuro-symbolic AI onto a silicon photonics substrate for IoT.
• The architecture offers substantial power efficiency, achieving a 20.8x improvement over ASIC baselines by using silicon photonics and eliminating costly ADCs.
• Implications include enhanced near-sensor processing, reduced latency, better privacy, and new avenues for symbolic AI using hyperdimensional computing within IoT systems.

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

The paper presents Neuro-Photonix, a distinctive near-sensor computing architecture that consolidates the capabilities of neuro-symbolic AI onto a silicon photonics substrate. The work addresses the challenges of integrating neuro-symbolic models within Internet of Things (IoT) nodes, aiming to efficiently process data near sensors while overcoming computational constraints traditionally associated with IoT devices.

Context and Motivation

Neuro-symbolic AI, which combines the pattern recognition prowess of neural networks with the reasoning and context understanding strength of symbolic AI, holds significant promise for IoT applications. These models are particularly beneficial for tasks necessitating transparent reasoning and robustness against data noise. Nevertheless, incorporating such models in IoT sensor nodes has been beset by issues tied to resource limitations, predominantly due to energy-intensive operations and the scale of required computations. This paper marks a novel undertaking to exploit silicon photonics for executing neuro-symbolic computations near the sensor, thereby reducing energy consumption and latency traditionally encountered with cloud-based processing paradigms.

Proposed Architecture

The Neuro-Photonix solution comprises several key components: a comprehensive sensor array, a low-overhead modulation unit (LMU), and a reconfigurable optical core (ROC). The LMU translates sensor data into a format suitable for photonic processing, bypassing the need for power-hungry ADCs by employing a novel Comparator-based Converter (CBC). This is coupled with a flexible light driving mechanism to modulate the light's intensity and wavelength encoded with data values. The ROC is the main computational unit, implementing neural dynamics through photonics with its ability to perform Multiply-Accumulate (MAC) operations in a highly parallel and energy-efficient manner.

The ROC processes neural dynamic computations analogously and supports HD vector generation utilisable in HD-based symbolic AI. This facilitates performing convolution operations finely tunable in granularity and hyperdimensional encoding efficiently intrinsic to the hardware's photonic capabilities.

Technical Advancements and Results

The proposed Neuro-Photonix architecture offers substantial benefits over traditional electronic solutions. It achieves significant gains in power efficiency, exhibiting a 20.8x improvement compared to ASIC baselines, achieving a power-performance metric of 30 GOPS/W. This is primarily facilitated through the silicon photonics' inherently high bandwidth and parallelism, surpassing electronic architectures' limitations in processing speed and power consumption.

The paper highlights a major design saving in eliminating the need for costly ADCs typically required in neuro-symbolic architectures, thus drastically diminishing energy costs and simplifying interconnect complexity between different layers and operations within the hardware.

Implications and Future Directions

Neuro-Photonix's architecture reflects a significant shift towards data-centric processing models within IoT systems, suggesting a future where devices can autonomously execute complex reasoning tasks near-sensor, minimizing data latency and offloading bandwidth demands from cloud infrastructures. The implications include enhanced privacy and security, given that intrinsic processing can occur locally, preventing unneeded data dissemination.

Furthermore, the integration of HD computing for symbolic AI tasks opens new avenues for expanding the role of hyperdimensional vectors in enabling robust and transparent decision-making processes. Future work could focus on advancing the symbolic layer within hardware, potentially leveraging neuromorphic approaches to mirror biological computational paradigms more closely in reasoning tasks.

Neuro-Photonix stands out as a forward-looking architecture marrying the best of AI’s cognitive capabilities with next-generation hardware capabilities, paving the way for more autonomous, efficient, and scalable IoT ecosystems.