Content addressable memory based binarized neural network accelerator using time-domain signal processing

Author:

Choi Woong1,Jeong Kwanghyo1,Choi Kyungrak1,Lee Kyeongho1,Park Jongsun1

Affiliation:

1. Korea University, Seoul, Korea

Funder

Ministry of Trade, Industry and Energy

National Research Foundation of Korea

Publisher

ACM

Reference11 articles.

1. Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

2. EIE: Efficient Inference Engine on Compressed Deep Neural Network

3. M. Courbariaux etal 2016. Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. (2016). arXiv:1602.02830. M. Courbariaux et al. 2016. Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. (2016). arXiv:1602.02830.

4. M. Rastegari etal 2016. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. In 2016 Computer Vision 525-542. M. Rastegari et al. 2016. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. In 2016 Computer Vision 525-542.

5. S. Zhou etal 2016. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. (2016). arXiv:1606.06160. S. Zhou et al. 2016. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. (2016). arXiv:1606.06160.

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