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Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendoussuccess in many machine learning applications. Nevertheless, the deep structurehas brought significant increases in computation complexity. Largescale deeplearning systems mainly operate in high-performance server clusters, thusrestricting the application extensions to personal or mobile devices. Previousworks on GPU and/or FPGA acceleration for DCNNs show increasing speedup, butignore other constraints, such as area, power, and energy. Stochastic Computing(SC), as a unique data representation and processing technique, has thepotential to enable the design of fully parallel and scalable hardwareimplementations of large-scale deep learning systems. This paper proposed anautomatic design allocation algorithm driven by budget requirement consideringoverall accuracy performance. This systematic method enables the automaticdesign of a DCNN where all design parameters are jointly optimized.Experimental results demonstrate that proposed algorithm can achieve a jointoptimization of all design parameters given the comprehensive budget of a DCNN.

Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing