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PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors

arXiv 1908.11263 ↗First posted 29 Aug 2019cs.NEcited by 34

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Abstract (arXiv)

We present PULP-NN, an optimized computing library for a parallel ultra-low-power tightly coupled cluster of RISC-V processors. The key innovation in PULP-NN is a set of kernels for Quantized Neural Network (QNN) inference, targeting byte and sub-byte data types, down to INT-1, tuned for the recent trend toward aggressive quantization in deep neural network inference. The proposed library exploits both the digital signal processing (DSP) extensions available in the PULP RISC-V processors and the cluster's parallelism, achieving up to 15.5 MACs/cycle on INT-8 and improving performance by up to 63x with respect to a sequential implementation on a single RISC-V core implementing the baseline RV32IMC ISA. Using PULP-NN, a CIFAR-10 network on an octa-core cluster runs in 30x and 19.6x less clock cycles than the current state-of-the-art ARM CMSIS-NN library, running on STM32L4 and STM32H7 MCUs, respectively. The proposed library, when running on GAP-8 processor, outperforms by 36.8x and by 7.45x the execution on energy efficient MCUs such as STM32L4 and high-end MCUs such as STM32H7 respectively, when operating at the maximum frequency. The energy efficiency on GAP-8 is 14.1x higher than STM32L4 and 39.5x higher than STM32H7, at the maximum efficiency operating point.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Aug 2019.

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Citing papers per year: 2021: 8; 2022: 7; 2023: 3; 2024: 7; 2025: 4; 2026: 5.

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  1. 2021Auto-Split: A General Framework of Collaborative Edge-Cloud AIcited by 29
  2. 2022Machine Learning for Microcontroller-Class Hardware: A Reviewcited by 28
  3. 2023A Machine Learning-oriented Survey on Tiny Machine Learningcited by 26
  4. 2021PhiNets: a scalable backbone for low-power AI at the edgecited by 16
  5. 2024MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AIcited by 15
  6. 2021ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Networkcited by 14
  7. 2021Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-designcited by 13
  8. 2021Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devicescited by 10
  9. 2023Enabling Resource-efficient AIoT System with Cross-level Optimization: A surveycited by 8
  10. 2024Optimizing the Deployment of Tiny Transformers on Low-Power MCUscited by 8
  11. 2022Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processorscited by 7
  12. 2025Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applicationscited by 6
22 more
  1. 2024MATCH: Model-Aware TVM-based Compilation for Heterogeneous Edge Devicescited by 5
  2. 2022TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inferencecited by 4
  3. 2024xTern: Energy-Efficient Ternary Neural Network Inference on RISC-V-Based Edge Systemscited by 4
  4. 2022Energy-Efficient Adaptive Machine Learning on IoT End-Nodes With Class-Dependent Confidencecited by 3
  5. 2025A Map-free Deep Learning-based Framework for Gate-to-Gate Monocular Visual Navigation aboard Miniaturized Aerial Vehiclescited by 3
  6. 2021Shifting Capsule Networks from the Cloud to the Deep Edgecited by 2
  7. 2021HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computationcited by 2
  8. 2022A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networkscited by 2
  9. 2024Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearablescited by 2
  10. 2025Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollerscited by 2
  11. 2021Compiler Toolchains for Deep Learning Workloads on Embedded Platformscited by 1
  12. 2023Energy-frugal and Interpretable AI Hardware Design using Learning Automatacited by 1
  13. 2025Decentor-V: Lightweight ML Training on Low-Power RISC-V Edge Devicescited by 1
  14. 2026FEMBA on the Edge: Physiologically-Aware Pre-Training, Quantization, and Deployment of a Bidirectional Mamba EEG Foundation Model on an Ultra-low Power Microcontrollercited by 1
  15. 2022Compilation and Optimizations for Efficient Machine Learning on Embedded Systemscited by 0
  16. 2022HiKonv: Maximizing the Throughput of Quantized Convolution With Novel Bit-wise Management and Computationcited by 0
  17. 2024Accelerating Depthwise Separable Convolutions on Ultra-Low-Power Devicescited by 0
  18. 2024Optimizing DNN Inference on Multi-Accelerator SoCs at Training-timecited by 0
  19. 2026End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearablescited by 0
  20. 2026OpenGlass: Ultra-Low-Power On-Device AI Eyewear with Event-based Visioncited by 0
  21. 2026Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision Systemcited by 0
  22. 2026Power-Performance Characterization of TinyML Systemscited by 0