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We present and experimentally validate two minimal compact memristive models for spiking neuronal signal generation using commercially available low-cost components. The first neuron model is called the Memristive Integrate-and-Fire (MIF) model, for neuronal signaling with two voltage levels: the spike-peak, and the rest-potential. The second model MIF2 is also presented, which promotes local adaptation by accounting for a third refractory voltage level during hyperpolarization. We show both compact models are minimal in terms of the number of circuit elements and integration area. Using the MIF and MIF2 models, we postulate the design of a memristive solid-state brain with an estimation of its surface area and power consumption. Analytical projections show that a memristive solid-state brain could be realized within (i) the surface area of the median human brain, 2,400cm2, (ii) the same volume of the median human brain, and (iii) a total power budget of approximately 20 W using a 3.5 nm technology. Distinct from the past decade of memristive neuron literature, our benchmarks are attained using generic commercially available memristors that are reproducible using off-the-shelf components. We expect this work can promote more experimental demonstrations of memristive circuits that do not rely on prohibitively expensive fabrication processes. [ABSTRACT FROM AUTHOR]

To address the von Neumann bottleneck that leads to both energy and speed degradations, in-memory processing architectures have been proposed as a promising alternative for future computing applications. In this paper, we present an in-memory computing system based on resistive random-access memory (RRAM) crossbar arrays that is reconfigurable and can potentially perform parallel and general computing tasks. The system consists of small look-up tables (LUTs), a memory block, and two search auxiliary blocks, all implemented in the same RRAM crossbar array. External data access and data conversions are eliminated to allow operations fully in-memory. Details of addition, AND logic and multiplication operations are discussed on the basis of search and writeback steps. A compact instruction set consisting of 10 instructions is demonstrated on this architecture through circuit level simulations. Performance evaluations show that the proposed in-memory computing architecture is suitable for handling data-intensive problems. The average power consumption of the crossbar chip is estimated to be $45~\mu \text{W}$. [ABSTRACT FROM AUTHOR]