# Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU

arXiv: https://arxiv.org/abs/2305.15422 · HTML: https://mlsanity.com/paper/2305.15422/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.03443/) (2018)
- [Population Based Training of Neural Networks](https://mlsanity.com/paper/1711.09846/) (2017)
- [Searching for Efficient Multi-Scale Architectures for Dense Image Prediction](https://mlsanity.com/paper/1809.04184/) (2018)
- [Going Deeper in Facial Expression Recognition using Deep Neural Networks](https://mlsanity.com/paper/1511.04110/) (2015)

## Built on by (most cited first, 6 of 6)

- [Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge AI Accelerators](https://mlsanity.com/paper/2403.08792/) (2024)
- [LLMPi: Optimizing LLMs for High-Throughput on Raspberry Pi](https://mlsanity.com/paper/2504.02118/) (2025)
- [Towards Efficient Deployment of Hybrid SNNs on Neuromorphic and Edge AI Hardware](https://mlsanity.com/paper/2407.08704/) (2024)
- [Flex-TPU: A Flexible TPU with Runtime Reconfigurable Dataflow Architecture](https://mlsanity.com/paper/2407.08700/) (2024)
- [Exploration of TPUs for AI Applications](https://mlsanity.com/paper/2309.08918/) (2023)
- [TransONet: Automatic Segmentation of Vasculature in Computed Tomographic Angiograms Using Deep Learning](https://mlsanity.com/paper/2311.10328/) (2023)

## Abstract (arXiv)

Facial Expression Recognition (FER) plays an important role in human-computer interactions and is used in a wide range of applications. Convolutional Neural Networks (CNN) have shown promise in their ability to classify human facial expressions, however, large CNNs are not well-suited to be implemented on resource- and energy-constrained IoT devices. In this work, we present a hierarchical framework for developing and optimizing hardware-aware CNNs tuned for deployment at the edge. We perform a comprehensive analysis across various edge AI accelerators including NVIDIA Jetson Nano, Intel Neural Compute Stick, and Coral TPU. Using the proposed strategy, we achieved a peak accuracy of 99.49% when testing on the CK+ facial expression recognition dataset. Additionally, we achieved a minimum inference latency of 0.39 milliseconds and a minimum power consumption of 0.52 Watts.