Supervised learning of DPLL based winner-take-all neural network

Abstract

Neural networks are widely used in various fields due to their superior learning abilities. This paper proposes a hardware winner-take-all neural network (WTANN) that employs a new winner-take-all (WTA) circuit with phase-modulated pulse signals and digital phase-locked loops (DPLLs). The system uses DPLL as a computing element, so all input values are expressed by phases of rectangular signals. The proposed WTA circuit employs a simple winner search circuit. The proposed WTANN architecture is described by very high speed integrated circuit (VHSIC) Hardware Description Language (VHDL) and its feasibility was tested and verified through simulations. Conventional WTA takes a centralized winner search approach, in which vector distances are collected from all neurons and compared. In contrast, the winner search in the proposed system is carried out locally by a distributed winner search circuit among neurons. Therefore, no global communication channels with a wide bandwidth between the winner search module and each neuron are required. Furthermore, the proposed WTANN can easily extend the system scale, merely by increasing the number of neurons. Vector classifications with WTANN using two kinds of data sets, Iris and Wine, were carried out in VHDL simulations. The circuit size and speed were then evaluated by applying the VHDL description to a logic synthesis tool and experiments using FPGA. The results revealed that the proposed WTANN achieved valid learning.