An On-Chip Learning Neuromorphic Autoencoder With Current-Mode Transposable Memory Read and Virtual Lookup Table

作者：Cho Hwasuk; Son Hyunwoo; Seong Kihwan; Kim Byungsub; Park Hong June; Sim Jae Yoon^*

来源：IEEE Transactions on Biomedical Circuits and Systems, 2018, 12(1): 161-170.

DOI：10.1109/TBCAS.2017.2762002

• Autoencoder
• deep belief network (DBN)
• neuromorphic
• on-chip learning
• restricted Boltzmann machine (RBM)
• unsupervised learning

This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 x 500 6b SRAM-based memory, the proposed architecture achieves simultaneous processing of multiplications and accumulations. In addition, a transposable memory read for both forward and backward propagations and a virtual lookup table are also proposed to perform an unsupervised learning of restricted Boltzmann machine. The IC is fabricated using 28-nm CMOS process and is verified in a three-layer network of encoder-decoder pair for training and recovery of images with two-dimensional 16 x 16 pixels. With a dataset of 50 digits, the IC shows a normalized root mean square error of 0.078. Measured energy efficiencies are 4.46 pJ per synaptic operation for inference and 19.26 pJ per synaptic weight update for learning, respectively. The learning performance is also estimated by simulations if the proposed hardware architecture is extended to apply to a batch training of 60 000 MNIST datasets.

• 出版日期2018-2