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Updated: Sep 21, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

An on-chip photonic deep neural network for image classification.

Farshid Ashtiani¹, Alexander J Geers¹, Firooz Aflatouni²

¹Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.

|June 1, 2022

Summary

Researchers developed an integrated photonic deep neural network (PDNN) for sub-nanosecond image classification. This novel approach bypasses traditional electronic limitations, enabling faster and more energy-efficient deep learning systems.

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Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics

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Published on: December 14, 2009

Area of Science:

Photonics
Deep Learning
Computer Vision

Background:

Deep neural networks (DNNs) are crucial for tasks like medical diagnosis but are limited by clock speed and memory access in electronic processors.
Existing photonic computation faces challenges in on-chip optical non-linearity and device loss, hindering the scalability of optical DNNs.

Purpose of the Study:

To report an integrated end-to-end photonic deep neural network (PDNN) capable of sub-nanosecond image classification.
To demonstrate a novel opto-electronic approach for neuron computation in photonic systems.

Main Methods:

Developed an integrated PDNN performing image classification by directly processing optical waves.
Implemented linear computation optically and non-linear activation opto-electronically within each neuron.
Utilized a uniformly distributed supply light for scalable per-neuron optical output.

Main Results:

Achieved image classification in under 570 picoseconds, comparable to a single clock cycle of digital platforms.
Demonstrated high accuracies for handwritten letter classification: >93.8% for two-class and >89.8% for four-class.
Showcased the scalability of PDNNs for large-scale applications.

Conclusions:

The developed PDNN offers a faster and more energy-efficient alternative to electronic deep learning systems.
Eliminating the need for analogue-to-digital conversion and large memory modules accelerates processing.
This clock-less optical data processing paves the way for next-generation deep learning systems.