YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights
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- Type
- article
- Published
- 2016-06-17
- Cited by
- 203
- References
- 38
- OpenAlex
- https://openalex.org/W2431931973
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10098855
Keywords
Convolutional neural network, Computer science, Application-specific integrated circuit, Power (physics), Chip
References
- Regularization of Neural Networks using DropConnect
- Deep Image: Scaling up Image Recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- BinaryConnect: Training Deep Neural Networks with binary weights during propagations
- Deep Speech: Scaling up end-to-end speech recognition
- A ultra-low-energy convolution engine for fast brain-inspired vision in multicore clusters
- NeuFlow: Dataflow vision processing system-on-a-chip
- Learning Hierarchical Features for Scene Labeling
- A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks
- ShiDianNao: Shifting vision processing closer to the sensor
- Origami: A Convolutional Network Accelerator
- 4.6 A1.93TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big-data applications
- Accelerating real-time embedded scene labeling with convolutional networks
- Convolution engine: balancing efficiency & flexibility in specialized computing
- NeuFlow: A runtime reconfigurable dataflow processor for vision
- Fractional Max-Pooling
- DeepFace: Closing the Gap to Human-Level Performance in Face Verification
- DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning
- Deep learning with COTS HPC systems
- ImageNet classification with deep convolutional neural networks
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- Computationally efficient target classification in multispectral image data with Deep Neural Networks
- SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing
- Cognitive computation and communication: A complement solution to cloud for IoT
- Hardware for machine learning: Challenges and opportunities
- FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
- Scaling Binarized Neural Networks on Reconfigurable Logic
- An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
- Efficient neural network acceleration on GPGPU using content addressable memory
- Efficient Processing of Deep Neural Networks: A Tutorial and Survey
- CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data
- Universal Source Coding of Deep Neural Networks
- LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks
- BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet
- Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
- A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
- Mixed Signal Neurocomputing Based on Floating-gate Memories
- Towards optimal quantization of neural networks
- Neural decoding of attentional selection in multi-speaker environments without access to clean sources
- Tactics to Directly Map CNN Graphs on Embedded FPGAs
- Randomized unregulated step descent for limited precision synaptic elements