mlsanityData to 7 Oct 2026

XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference

arXiv 1807.03010 ↗First posted 9 Jul 2018cs.NE cs.AR cs.LGcited by 28

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Abstract (arXiv)

Binary Neural Networks (BNNs) are promising to deliver accuracy comparable to conventional deep neural networks at a fraction of the cost in terms of memory and energy. In this paper, we introduce the XNOR Neural Engine (XNE), a fully digital configurable hardware accelerator IP for BNNs, integrated within a microcontroller unit (MCU) equipped with an autonomous I/O subsystem and hybrid SRAM / standard cell memory. The XNE is able to fully compute convolutional and dense layers in autonomy or in cooperation with the core in the MCU to realize more complex behaviors. We show post-synthesis results in 65nm and 22nm technology for the XNE IP and post-layout results in 22nm for the full MCU indicating that this system can drop the energy cost per binary operation to 21.6fJ per operation at 0.4V, and at the same time is flexible and performant enough to execute state-of-the-art BNN topologies such as ResNet-34 in less than 2.2mJ per frame at 8.9 fps.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jul 2018.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 4; 2024: 2; 2025: 7; 2026: 3.

28 papers.

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  1. 2022Hardware Approximate Techniques for Deep Neural Network Accelerators: A Surveycited by 39
  2. 2021Quantization Backdoors to Deep Learning Commercial Frameworkscited by 25
  3. 2022Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inferencecited by 16
  4. 2021Quantum Annealing Formulation for Binary Neural Networkscited by 13
  5. 2021RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracycited by 11
  6. 2022Distributed On-Sensor Compute System for AR/VR Devices: A Semi-Analytical Simulation Framework for Power Estimationcited by 8
  7. 2023Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC with 2-to-8b DNN Acceleration and 30%-Boost Adaptive Body Biasingcited by 8
  8. 2022Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processorscited by 7
  9. 2025Benchmarking Ultra-Low-Power μNPUscited by 6
  10. 2024Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envisioncited by 5
  11. 2023RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Accelerationcited by 4
  12. 2021RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Thingscited by 3
16 more
  1. 2025An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Acceleratorcited by 3
  2. 2025On Space Folds of ReLU Neural Networkscited by 3
  3. 2021Training Multi-bit Quantized and Binarized Networks with A Learnable Symmetric Quantizercited by 2
  4. 2022Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inferencecited by 2
  5. 2022PalQuant: Accelerating High-precision Networks on Low-precision Acceleratorscited by 2
  6. 2025An Open-Source HW-SW Co-Development Framework Enabling Efficient Multi-Accelerator Systemscited by 2
  7. 2023Energy-frugal and Interpretable AI Hardware Design using Learning Automatacited by 1
  8. 2024Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flowcited by 1
  9. 2025Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAscited by 1
  10. 2025Stochastic Engrams for Efficient Continual Learningcited by 1
  11. 2021A Construction Kit for Efficient Low Power Neural Network Accelerator Designscited by 0
  12. 2023Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelinescited by 0
  13. 2025CAMformer: Associative Memory is All You Needcited by 0
  14. 2026PiC-BNN: A 128-kbit 65 nm Processing-in-CAM-Based End-to-End Binary Neural Network Acceleratorcited by 0
  15. 2026Active Continual Learning with Metaplastic Binary Bayesian Neural Networkscited by 0
  16. 2026A Motion-Aware Vector Quantization Framework with Centroid Reuse for Efficient VLA Inferencecited by 0