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DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks

by deepx
Abstract—Although there have been many decades of research and commercial presence on high performance general purpose
processors, there are still many applications that require fully customized hardware architectures for further computational
acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy
computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration
architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann
machine (RBM) ANNs. The implemented RBM ANN accelerator (integrating 1024 ×1024 network size, using 128 input cases per
batch, and running at a 303-MHz clock frequency) integrated in a state-of-the art field-programmable gate array (FPGA)
(Xilinx Virtex 7 XC7V-2000T) provides a computational performance of 301-billion connection-updates-per-second and
about 193 times higher performance than a software solution running on general purpose processors. Most importantly, the
architecture enables over 4 times (12 times in batch learning) higher performance compared with a previous work when both
are implemented in an FPGA device (XC2VP70).
Patent Section
AI Applications0+AI Memory Architecture0+AI Vision/ISP0+NPU0+Trade Mark0+AR/VR Applications0+
Total Approved & Pending