Browsing by Author "Yu, Wan-Ju"

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A 2 GOPS Quad-Mean Shift Processor with Early Termination for Machine Learning Applications
(2014-01-01)
Tsai, Chang-Hung
;
Lee, Hui-Hsuan
;
Yu, Wan-Ju
;
Lee, Chen-Yi
;
電機工程學系
This paper proposes a 2 GOPS quad-mean shift processor (Q-MSP) architecture for data clustering and machine learning applications. By exploiting the linear approximation approach and early termination mechanism, the proposed algorithm can reduce 70% and 40% computational complexity, respectively. Moreover, 4 mean shift processor cores are integrated into the proposed architecture to support parallel processing to further improve system performance. Implemented in Xilinx Virtex-7 FPGA, this architecture occupies 65k LUTs and 3.3MB block memory to achieve 2 GOPS throughput operated at 125MHz.
A 41.3/26.7 pJ per Neuron Weight RBM Processor Supporting On-Chip Learning/Inference for IoT Applications
(2017-10-01)
Tsai, Chang-Hung
;
Yu, Wan-Ju
;
Wong, Wing Hung
;
Lee, Chen-Yi
;
電子工程學系及電子研究所
An energy-efficient restricted Boltzmann machine (RBM) processor (RBM-P) supporting on-chip learning and inference is proposed for machine learning and Internet of Things (IoT) applications in this paper. To train a neural network (NN) model, the RBM structure is applied to supervised and unsupervised learning, and a multi-layer NN can be constructed and initialized by stacking multiple RBMs. Featuring NN model reduction for external memory bandwidth saving, low power neuron binarizer (LPNB) with dynamic clock gating and area-efficient NN-like activation function calculators for power reduction, user-defined connection map (UDCM) for both computation time and bandwidth saving, and early stopping (ES) mechanism for learning process, the proposed system integrates 32 RBM cores with maximal 4k neurons per layer and 128 candidates per sample for machine learning applications. Implemented in 65nm CMOS technology, the proposed RBM-P chip costs 2.2 M gates and 128 kB SRAM with 8.8 mm(2) area. Operated at 1.2 V and 210 MHz, this chip achieves 7.53G neuron weights (NWs) and 11.63G NWs per second with 41.3 and 26.7 pJ per NW for learning and inference, respectively.