# An Electro-Photonic System for Accelerating Deep Neural Networks

arXiv: https://arxiv.org/abs/2109.01126 · HTML: https://mlsanity.com/paper/2109.01126/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2021.

## Built on by (most cited first, 15 of 15)

- [Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics](https://mlsanity.com/paper/2301.12252/) (2023)
- [M3ICRO: Machine Learning-Enabled Compact Photonic Tensor Core based on PRogrammable Multi-Operand Multimode Interference](https://mlsanity.com/paper/2305.19505/) (2023)
- [SCONNA: A Stochastic Computing Based Optical Accelerator for Ultra-Fast, Energy-Efficient Inference of Integer-Quantized CNNs](https://mlsanity.com/paper/2302.07036/) (2023)
- [A Blueprint for Precise and Fault-Tolerant Analog Neural Networks](https://mlsanity.com/paper/2309.10759/) (2023)
- [AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks](https://mlsanity.com/paper/2210.10048/) (2022)
- [Mirage: An RNS-Based Photonic Accelerator for DNN Training](https://mlsanity.com/paper/2311.17323/) (2023)
- [Scalable Back-Propagation-Free Training of Optical Physics-Informed Neural Networks](https://mlsanity.com/paper/2502.12384/) (2025)
- [INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers](https://mlsanity.com/paper/2307.03712/) (2023)
- [Photonics for Sustainable Computing](https://mlsanity.com/paper/2401.05121/) (2024)
- [Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection](https://mlsanity.com/paper/2309.16783/) (2023)
- [Towards Efficient Hyperdimensional Computing Using Photonics](https://mlsanity.com/paper/2311.17801/) (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)
- [Harnessing Photonics for Machine Intelligence](https://mlsanity.com/paper/2604.10841/) (2026)
- [LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks](https://mlsanity.com/paper/2606.10294/) (2026)

## Abstract (arXiv)

The number of parameters in deep neural networks (DNNs) is scaling at about 5× the rate of Moore's Law. To sustain this growth, photonic computing is a promising avenue, as it enables higher throughput in dominant general matrix-matrix multiplication (GEMM) operations in DNNs than their electrical counterpart. However, purely photonic systems face several challenges including lack of photonic memory and accumulation of noise. In this paper, we present an electro-photonic accelerator, ADEPT, which leverages a photonic computing unit for performing GEMM operations, a vectorized digital electronic ASIC for performing non-GEMM operations, and SRAM arrays for storing DNN parameters and activations. In contrast to prior works in photonic DNN accelerators, we adopt a system-level perspective and show that the gains while large are tempered relative to prior expectations. Our goal is to encourage architects to explore photonic technology in a more pragmatic way considering the system as a whole to understand its general applicability in accelerating today's DNNs. Our evaluation shows that ADEPT can provide, on average, 5.73× higher throughput per Watt compared to the traditional systolic arrays (SAs) in a full-system, and at least 6.8× and 2.5× better throughput per Watt, compared to state-of-the-art electronic and photonic accelerators, respectively.