mlsanityData to 7 Oct 2026

TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT

arXiv 2104.02233 ↗First posted 6 Apr 2021cs.LG cs.AIcited by 4

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Abstract (arXiv)

In this research, we propose a new low-precision framework, TENT, to leverage the benefits of a tapered fixed-point numerical format in TinyML models. We introduce a tapered fixed-point quantization algorithm that matches the numerical format's dynamic range and distribution to that of the deep neural network model's parameter distribution at each layer. An accelerator architecture for the tapered fixed-point with TENT framework is proposed. Results show that the accuracy on classification tasks improves up to ~31 % with an energy overhead of ~17-30 % as compared to fixed-point, for ConvNet and ResNet-18 models.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 6 Apr 2021.

Builds on

23 of 38 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  3. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
  4. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  5. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 937
  6. 2020Scaling Laws for Autoregressive Generative Modelingcited by 510
  7. 2018Quantizing deep convolutional networks for efficient inference: A whitepapercited by 507
  8. 2018PACT: Parameterized Clipping Activation for Quantized Neural Networkscited by 456
  9. 2018HAQ: Hardware-Aware Automated Quantization with Mixed Precisioncited by 426
  10. 2020MCUNet: Tiny Deep Learning on IoT Devicescited by 269
  11. 2019HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networkscited by 250
  12. 2018CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUscited by 116
11 more
  1. 2017Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracycited by 100
  2. 2020Benchmarking TinyML Systems: Challenges and Directioncited by 89
  3. 2018DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learningcited by 69
  4. 2020μNAS: Constrained Neural Architecture Search for Microcontrollerscited by 54
  5. 2019SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollerscited by 52
  6. 2019Visual Wake Words Datasetcited by 46
  7. 2018Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inferencecited by 33
  8. 2018SCALE-Sim: Systolic CNN Accelerator Simulatorcited by 26
  9. 2020Least squares binary quantization of neural networkscited by 21
  10. 2020Leveraging Automated Mixed-Low-Precision Quantization for tiny edge microcontrollerscited by 9
  11. 2020Compressed Deep Networks: Goodbye SVD, Hello Robust Low-Rank Approximationcited by 8

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1.

4 papers.

  1. 2022On-Device Training Under 256KB Memorycited by 158
  2. 2021MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learningcited by 87
  3. 2024Tiny Machine Learning: Progress and Futurescited by 40
  4. 2025From Tiny Machine Learning to Tiny Deep Learning: A Surveycited by 5