In-memory computing is an attractive solution for reducing power consumption and memory access latency cost by performing certain computations directly in memory without reading operands and sending them to arithmetic logic units. Content-addressable memory (CAM) is an ideal way to smooth out the distinction between storage and processing, since each memory cell is a processing unit. CAM compares the search input with a table of stored data and returns the matched data address. The issues of constructing binary and ternary content-addressable memory (CAM and TCAM) based on ferroelectric devices are considered. A review of ferroelectric materials and devices is carried out, including on ferroelectric transistors (FeFET), ferroelectric tunnel diodes (FTJ), and ferroelectric memristors.

A new algorithm (NWTA algorithm) for solving the traveling salesman problem (TSP) is proposed. The algorithm is based on the use of the Hopfield recurrent neural network, the “Winner takes all” (WTA) method for the cycle formation, and the 2-opt optimization method. A special feature of the algorithm proposed is in the use of the method of partial (prefix) sums to accelerate the solution of the system of the Hopfield network equations. For attaining additional acceleration, the parallelization of the algorithm proposed is performed on GPU with the CUDA technology. Several examples from the TSPLIB library with the number of cities from 51 to 2,392 show that the algorithm finds approximate solutions of the TSP (a relative increase in the length of the route with respect to the optimum is 0.03 ÷ 0.14). With a large number of cities (130 and more), the NWTA algorithm running duration on the CPU is 4 ÷ 24 times shorter than the duration of the heuristic LKH algorithm giving optimal solutions for all TSPLIB examples.

An algorithm based on a recurrent neural Wang’s network and the WTA (“Winner takes all”) principle is applied to the construction of Hamiltonian cycles in graphs of distributed computer systems (CSs). The algorithm is used for: 1) regular graphs (2D- and 3D-tori, and hypercubes) of distributed CSs and 2) 2D-tori disturbed by removing an arbitrary edge. The neural network parameters for the construction of Hamiltonian cycles and suboptimal cycles with a length close to that of Hamiltonian ones are determined. Our experiments show that the iterative method (Jacobi, Gauss-Seidel, or SOR) used for solving the system of differential equations describing a neural network strongly affects the process of cycle construction and depends on the number of torus nodes.

An approach to a quantitative evaluation of heterogeneity ("motley") of thermal images (thermograms) bated on the wavelet Haar transform is proposed and investigated. It is shown that the Haar transform allows getting the evaluation to be adequate to visual perception of the thermogram heterogeneity.