mlsanityData to 7 Oct 2026

TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference

arXiv 2203.12925 ↗First posted 24 Mar 2022cs.LG cs.AIcited by 4

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Abstract (arXiv)

Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series analysis. We introduce an automated exploration approach and a library of optimized kernels to map TCNs on Parallel Ultra-Low Power (PULP) microcontrollers. Our approach minimizes latency and energy by exploiting a layer tiling optimizer to jointly find the tiling dimensions and select among alternative implementations of the causal and dilated 1D-convolution operations at the core of TCNs. We benchmark our approach on a commercial PULP device, achieving up to 103X lower latency and 20.3X lower energy than the Cube-AI toolkit executed on the STM32L4 and from 2.9X to 26.6X lower energy compared to commercial closed-source and academic open-source approaches on the same hardware target.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 14 references that are in the map. First posted on arXiv 24 Mar 2022.

Builds on

11 of 14 references.

References · 11

  1. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  2. 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 610
  3. 2016Temporal Convolutional Networks: A Unified Approach to Action Segmentationcited by 199
  4. 2018CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUscited by 116
  5. 2020DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUscited by 57
  6. 2019PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processorscited by 34
  7. 2019Optimizing Speech Recognition For The Edgecited by 32
  8. 2019FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Thingscited by 17
  9. 2016A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devicescited by 12
  10. 2019The Indirect Convolution Algorithmcited by 7
  11. 2019Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontrollercited by 4

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.

4 papers.

  1. 2023Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edgecited by 15
  2. 2022Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networkscited by 6
  3. 2022Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNscited by 2
  4. 2025TinyD'ej`aVu: Smaller RAM and Faster Inference with Neural Networks on MCUs for Sensor Data Streamscited by 1