# An Optical XNOR-Bitcount Based Accelerator for Efficient Inference of Binary Neural Networks

arXiv: https://arxiv.org/abs/2302.06405 · HTML: https://mlsanity.com/paper/2302.06405/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [Deep Learning with Limited Numerical Precision](https://mlsanity.com/paper/1502.02551/) (2015)
- [LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks](https://mlsanity.com/paper/1807.10029/) (2018)
- [Compressing Deep Convolutional Networks using Vector Quantization](https://mlsanity.com/paper/1412.6115/) (2014)
- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1](https://mlsanity.com/paper/1602.02830/) (2016)
- [Towards Unified INT8 Training for Convolutional Neural Network](https://mlsanity.com/paper/1912.12607/) (2019)
- [Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)](https://mlsanity.com/paper/1907.01525/) (2019)
- [ROBIN: A Robust Optical Binary Neural Network Accelerator](https://mlsanity.com/paper/2107.05530/) (2021)
- [Photonic Reconfigurable Accelerators for Efficient Inference of CNNs with Mixed-Sized Tensors](https://mlsanity.com/paper/2207.05278/) (2022)
- [PROTEUS: Rule-Based Self-Adaptation in Photonic NoCs for Loss-Aware Co-Management of Laser Power and Performance](https://mlsanity.com/paper/2008.07566/) (2020)

## Built on by (most cited first, 3 of 3)

- [FwdLLM: Efficient FedLLM using Forward Gradient](https://mlsanity.com/paper/2308.13894/) (2023)
- [High-Speed and Energy-Efficient Non-Binary Computing with Polymorphic Electro-Optic Circuits and Architectures](https://mlsanity.com/paper/2304.07608/) (2023)
- [HEANA: A Hybrid Time-Amplitude Analog Optical Accelerator with Flexible Dataflows for Energy-Efficient CNN Inference](https://mlsanity.com/paper/2402.03247/) (2024)

## Abstract (arXiv)

Binary Neural Networks (BNNs) are increasingly preferred over full-precision Convolutional Neural Networks(CNNs) to reduce the memory and computational requirements of inference processing with minimal accuracy drop. BNNs convert CNN model parameters to 1-bit precision, allowing inference of BNNs to be processed with simple XNOR and bitcount operations. This makes BNNs amenable to hardware acceleration. Several photonic integrated circuits (PICs) based BNN accelerators have been proposed. Although these accelerators provide remarkably higher throughput and energy efficiency than their electronic counterparts, the utilized XNOR and bitcount circuits in these accelerators need to be further enhanced to improve their area, energy efficiency, and throughput. This paper aims to fulfill this need. For that, we invent a single-MRR-based optical XNOR gate (OXG). Moreover, we present a novel design of bitcount circuit which we refer to as Photo-Charge Accumulator (PCA). We employ multiple OXGs in a cascaded manner using dense wavelength division multiplexing (DWDM) and connect them to the PCA, to forge a novel Optical XNOR-Bitcount based Binary Neural Network Accelerator (OXBNN). Our evaluation for the inference of four modern BNNs indicates that OXBNN provides improvements of up to 62x and 7.6x in frames-per-second (FPS) and FPS/W (energy efficiency), respectively, on geometric mean over two PIC-based BNN accelerators from prior work. We developed a transaction-level, event-driven python-based simulator for evaluation of accelerators (https://github.com/uky-UCAT/B_ONN_SIM).