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There is a pressing need to build an architecture that could subsume thesenetworks under a unified framework that achieves both higher performance andless overhead. To this end, two fundamental issues are yet to be addressed. Thefirst one is how to implement the back propagation when neuronal activationsare discrete. The second one is how to remove the full-precision hidden weightsin the training phase to break the bottlenecks of memory/computationconsumption. To address the first issue, we present a multi-step neuronalactivation discretization method and a derivative approximation technique thatenable the implementing the back propagation algorithm on discrete DNNs. Whilefor the second issue, we propose a discrete state transition (DST) methodologyto constrain the weights in a discrete space without saving the hidden weights.Through this way, we build a unified framework that subsumes the binary orternary networks as its special cases, and under which a heuristic algorithm isprovided at the website https://github.com/AcrossV/Gated-XNOR. Moreparticularly, we find that when both the weights and activations become ternaryvalues, the DNNs can be reduced to sparse binary networks, termed as gated XNORnetworks (GXNOR-Nets) since only the event of non-zero weight and non-zeroactivation enables the control gate to start the XNOR logic operations in theoriginal binary networks. This promises the event-driven hardware design forefficient mobile intelligence. We achieve advanced performance compared withstate-of-the-art algorithms. Furthermore, the computational sparsity and thenumber of states in the discrete space can be flexibly modified to make itsuitable for various hardware platforms.

GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework