# SCONNA: A Stochastic Computing Based Optical Accelerator for Ultra-Fast, Energy-Efficient Inference of Integer-Quantized CNNs

arXiv: https://arxiv.org/abs/2302.07036 · HTML: https://mlsanity.com/paper/2302.07036/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 66 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit](https://mlsanity.com/paper/2008.11659/) (2020)
- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator](https://mlsanity.com/paper/2102.06960/) (2021)
- [Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)](https://mlsanity.com/paper/1907.01525/) (2019)
- [An Electro-Photonic System for Accelerating Deep Neural Networks](https://mlsanity.com/paper/2109.01126/) (2021)
- [SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing](https://mlsanity.com/paper/1611.05939/) (2016)
- [Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors](https://mlsanity.com/paper/2104.09252/) (2021)
- [Photonic Reconfigurable Accelerators for Efficient Inference of CNNs with Mixed-Sized Tensors](https://mlsanity.com/paper/2207.05278/) (2022)

## Built on by (most cited first, 4 of 4)

- [High-Speed and Energy-Efficient Non-Binary Computing with Polymorphic Electro-Optic Circuits and Architectures](https://mlsanity.com/paper/2304.07608/) (2023)
- [A Comparative Analysis of Microrings Based Incoherent Photonic GEMM Accelerators](https://mlsanity.com/paper/2402.03149/) (2024)
- [HEANA: A Hybrid Time-Amplitude Analog Optical Accelerator with Flexible Dataflows for Energy-Efficient CNN Inference](https://mlsanity.com/paper/2402.03247/) (2024)
- [Sustainable Transformer Neural Network Acceleration with Stochastic Photonic Computing](https://mlsanity.com/paper/2604.09759/) (2026)

## Abstract (arXiv)

The acceleration of a CNN inference task uses convolution operations that are typically transformed into vector-dot-product (VDP) operations. Several photonic microring resonators (MRRs) based hardware architectures have been proposed to accelerate integer-quantized CNNs with remarkably higher throughput and energy efficiency compared to their electronic counterparts. However, the existing photonic MRR-based analog accelerators exhibit a very strong trade-off between the achievable input/weight precision and VDP operation size, which severely restricts their achievable VDP operation size for the quantized input/weight precision of 4 bits and higher. The restricted VDP operation size ultimately suppresses computing throughput to severely diminish the achievable performance benefits. To address this shortcoming, we for the first time present a merger of stochastic computing and MRR-based CNN accelerators. To leverage the innate precision flexibility of stochastic computing, we invent an MRR-based optical stochastic multiplier (OSM). We employ multiple OSMs in a cascaded manner using dense wavelength division multiplexing, to forge a novel Stochastic Computing based Optical Neural Network Accelerator (SCONNA). SCONNA achieves significantly high throughput and energy efficiency for accelerating inferences of high-precision quantized CNNs. Our evaluation for the inference of four modern CNNs at 8-bit input/weight precision indicates that SCONNA provides improvements of up to 66.5x, 90x, and 91x in frames-per-second (FPS), FPS/W and FPS/W/mm2, respectively, on average over two photonic MRR-based analog CNN accelerators from prior work, with Top-1 accuracy drop of only up to 0.4% for large CNNs and up to 1.5% for small CNNs. We developed a transaction-level, event-driven python-based simulator for the evaluation of SCONNA and other accelerators (https://github.com/uky-UCAT/SC_ONN_SIM.git).