Chipmunk: A systolically scalable 0.9 mm 2 , 3.08Gop/s/mW @ 1.2 mW accelerator for near-sensor recurrent neural network inference

Conference object, Preprint English OPEN
Conti, Francesco; Cavigelli, Lukas; Paulin, Gianna; Susmelj, Igor; Benini, Luca;
(2017)
  • Publisher: IEEE
  • Related identifiers: doi: 10.1109/cicc.2018.8357068, doi: 10.3929/ethz-b-000272086
  • Subject: Computer Science - Distributed, Parallel, and Cluster Computing | Computer Science - Sound | Hardware and Architecture | Electrical and Electronic Engineering | Electronic, Optical and Magnetic Materials | Computer Science - Neural and Evolutionary Computing | Computer Science - Learning

Recurrent neural networks (RNNs) are state-of-the-art in voice awareness/understanding and speech recognition. On-device computation of RNNs on low-power mobile and wearable devices would be key to applications such as zero-latency voice-based human-machine interfaces. ... View more
  • References (17)
    17 references, page 1 of 2

    A. Graves, A.-R. Mohamed, and G. Hinton, “Speech Recognition With Deep Recurrent Neural Networks,” in Proc. IEEE ICASSP, 2013.

    W. Xiong, J. Droppo et al., “The Microsoft 2016 Conversational Speech Recognition System,” in Proc. IEEE ICASSP, 2017, pp. 5255-5259.

    K. Cho, B. van Merrienboer et al., “Learning Phrase Representations using RNN EncoderDecoder for Statistical Machine Translation,” in Proc. ACL EMNLP, 2014, pp. 1724-1734.

    L. Cavigelli and L. Benini, “A 803 GOp/s/W Convolutional Network Accelerator,” IEEE TCSVT, 2016.

    F. Conti, R. Schilling et al., “An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics,” IEEE TCAS, vol. 64, no. 9, pp. 2481-2494, 9 2017.

    R. Andri, L. Cavigelli et al., “YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration,” IEEE TCAD, 2017.

    IEEE ISSCC, 2016, pp. 262-263.

    Z. Du, R. Fasthuber et al., “ShiDianNao: Shifting Vision Processing Closer to the Sensor,” in Proc. ACM/IEEE ISCA, 2015, pp. 92-104.

    V. Sze, Y.-H. Chen et al., “Efficient Processing of Deep Neural Networks: A Tutorial and Survey,” arXiv:703.09039, 2017.

    N. P. Jouppi, A. Borchers et al., “In-Datacenter Performance Analysis of a Tensor Processing Unit,” in Proc. ACM ISCA, 2017.

  • Metrics
Share - Bookmark