Neuron inspired data encoding memristive multi-level memory cell

Abstract

Mapping neuro-inspired algorithms to sensor backplanes of on-chip hardware require shifting the signal processing from digital to the analog domain, demanding memory technologies beyond conventional CMOS binary storage units. Using memristors for building analog data storage is one of the promising approaches amongst emerging non-volatile memory technologies. Recently, a memristive multi-level memory (MLM) cell for storing discrete analog values has been developed in which memory system is implemented combining memristors in voltage divider configuration. In given example, the memory cell of 3 sub-cells with a memristor in each was programmed to store ternary bits which overall achieved 10 and 27 discrete voltage levels. However, for further use of proposed memory cell in analog signal processing circuits data encoder is required to generate control voltages for programming memristors to store discrete analog values. In this paper, we present the design and performance analysis of data encoder that generates write pattern signals for 10 level memristive memory.

Notice of Retraction: Implementation of True Random Number Generator based on Double-Scroll Attractor circuit with GST memristor emulator

“…Memristors are differed from each other and classified according to the technology of creation, materials and related features. In this paper we propose a design of TRNG circuit that is based on Phase-Change Memory device (Ge 2 Sb 2 T e 2 ), which is known as GST memristor [4], [5], [6], [7]. For the present time there is no existing GST memristor model, that is why its characteristical equations and parameters should be investigated in order to create its emulator circuit.…”

Notice of Retraction: Analysis of Noise in Current Mirrors with Memristive Device

“…When current is no longer applied its oxide layer remains on that particular level and resistance stays fixed at a new value. This effect can be used for non-volatile memory and neuromorphic circuits [3]. In software memristor models are developed mostly by utilizing a concept of parallel switching channels that can have on and off states [4], [5].…”

Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits

“…The backpropagation process through the output layer is implemented using MB2 and MAIN BLOCK 4 (MB4), and the backpropagation through the hidden layer corresponds to the MAIN BLOCK 3 (MB3). The possible architecture of analog memory unit is illustrated in [40], [52], [53].…”