mlsanityData to 14 Oct 2026

Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations

arXiv 2206.10200 ↗First posted 21 Jun 2022cs.LGcited by 2

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Abstract (arXiv)

Complex Deep Neural Networks such as Capsule Networks (CapsNets) exhibit high learning capabilities at the cost of compute-intensive operations. To enable their deployment on edge devices, we propose to leverage approximate computing for designing approximate variants of the complex operations like softmax and squash. In our experiments, we evaluate tradeoffs between area, power consumption, and critical path delay of the designs implemented with the ASIC design flow, and the accuracy of the quantized CapsNets, compared to the exact functions.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 24 references that are in the map. First posted on arXiv 21 Jun 2022.

Builds on

12 of 24 references.

References · 12

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  2. 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,262
  3. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 2,031
  4. 2017Dynamic Routing Between Capsulescited by 689
  5. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 403
  6. 2020A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospectscited by 397
  7. 2017Adaptive Quantization for Deep Neural Networkcited by 67
  8. 2019DeepCaps: Going Deeper with Capsule Networkscited by 42
  9. 2019ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retrainingcited by 28
  10. 2019ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximationscited by 7
  11. 2020Q-CapsNets: A Specialized Framework for Quantizing Capsule Networkscited by 6
  12. 2018CapsAcc: An Efficient Hardware Accelerator for CapsuleNets with Data Reusecited by 4

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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantizationcited by 4
  2. 2023RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attackscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2021Shifting Capsule Networks from the Cloud to the Deep Edge
  2. 2026A Comparative Study of CNN Optimization Methods for Edge AI: Exploring the Role of Early Exits
  3. 2024Quality Scalable Quantization Methodology for Deep Learning on Edge
  4. 2024David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge
  5. 2022Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge
  6. 2024Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks
  7. 2025MiCo: End-to-End Mixed Precision Neural Network Co-Exploration Framework for Edge AI
  8. 2023Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge