Noise and Memristance Variation Tolerance of Single Crossbar Architectures for Neuromorphic Image Recognition

We performed a comparative study on the Gaussian noise and memristance variation tolerance of three crossbar architectures, namely the complementary crossbar architecture, the twin crossbar architecture, and the single crossbar architecture, for neuromorphic image recognition and conducted an experiment to determine the performance of the single crossbar architecture for simple pattern recognition. Ten grayscale images with the size of 32×32 pixels were used for testing and comparing the recognition rates of the three architectures. The recognition rates of the three memristor crossbar architectures were compared to each other when the noise level of images was varied from -10 to 4 dB and the percentage of memristance variation was varied from 0% to 40%. The simulation results showed that the single crossbar architecture had the best Gaussian noise input and memristance variation tolerance in terms of recognition rate. At the signal-to-noise ratio of –10 dB, the single crossbar architecture produced a recognition rate of 91%, which was 2% and 87% higher than those of the twin crossbar architecture and the complementary crossbar architecture, respectively. When the memristance variation percentage reached 40%, the single crossbar architecture had a recognition rate as high as 67.8%, which was 1.8% and 9.8% higher than the recognition rates of the twin crossbar architecture and the complementary crossbar architecture, respectively. Finally, we carried out an experiment to determine the performance of the single crossbar architecture with a fabricated 3 × 3 memristor crossbar based on carbon fiber and aluminum film. The experiment proved successful implementation of pattern recognition with the single crossbar architecture.

Automation of irrigation by electronic tensiometry based on the arduino hardware platform

This study developed and evaluated an electronic irrigation system controlled by soil water matric potential. The controller uses tensiometers and pressure transducers as a reading mechanism, integrated with an Arduino microcontroller board that drives the solenoid valves and a 1/3 hp single-phase motor. Four electronic tensiometers were installed in plastic containers filled with 6 kg of Red-Yellow Latosol (RYL) with a clayey texture, and another four in plastic containers filled with 7 kg of Regolitic Neosol (RN) with a sandy texture. Irrigation automation components were activated autonomously at the critical potentials of -20, -25, -30, and -35 kPa for RYL, and -10, -15, -20, and -25 kPa for RN, with a 20% variation tolerance. The entire system is able to monitor and control irrigation based on soil water matric potential. Components were deactivated when the soil water potential reached the field capacity of each soil type. Irrigation automation performance was considered satisfactory, as it kept critical potentials within the pre-established thresholds in both soil types. Automation control was set for matric potentials between -10 kPa and -35 kPa in RYL, and between -5 kPa and -25 kPa in RN.

Design of a Stable Low Power 11-T Static Random Access Memory Cell

In this paper, a 11-T static random-access memory (SRAM) cell has been examined that shows a fair reduction in read power dissipation while upholding the stability and moderate performance. In the presented work, parametric variability analysis of various design metrices such as signal to noise margin, read current and read power of the Proposed 11T cell are presented and compared with few considered topologies. The Proposed cell offers single ended write operation and differential read operation. The improvement in read signal to noise margin and write signal to noise margin with respect to conventional 6T SRAM is 10.63% and 33.09%, respectively even when the write operation is single ended. Mean hold static noise margin of the cell for 3000 samples is [Formula: see text] times higher than considered D2p11T cell. Sensitivity analysis of data retention voltage (DRV) with respect to temperature variations is also investigated and compared with considered topologies. DRV variation with temperature is least in FF process corner. In comparison to conventional 6T SRAM cell, the write and read delay of Proposed 11T cell gets improved by [Formula: see text] and 1.64%, respectively. Proposed 11T topology consumes least read energy in comparison with considered topologies. In comparison with another considered 11T topology, i.e., D2p11T cell, Proposed cell consumes 13.11% lesser area. Process variation tolerance with Monte Carlo simulation for read current and read power has been investigated using Cadence virtuoso tool with GPDK 45-nm technology.