A Fully Differential CMOS Potentiostat

A CMOS potentiostat for chemical sensing in a noisy environment is presented. The potentiostat measures bidirectional electrochemical redox currents proportional to the concentration of a chemical down to pico-ampere range. The fully differential architecture with differential recording electrodes suppresses the common mode interference. A 200μm×200μm prototype was fabricated in a standard 0.35μm standard CMOS technology and yields a 70dB dynamic range. The in-channel analog-to-digital converter (ADC) performs 16-bit current-tofrequency quantization. The integrated potentiostat functionality is validated in electrical and electrochemical experiments

Simulation of memristive crossbar arrays for seizure detection and prediction using parallel Convolutional Neural Networks [Formula presented]

For epileptic seizure detection and prediction, to address the computational bottleneck of the von Neumann architecture, we develop an in-memory memristive crossbar-based accelerator simulator. The simulator software is composed of a Python-based neural network training component and a MATLAB-based memristive crossbar array component. The software provides a baseline network for developing deep learning-based signal processing tasks, as well as a platform to investigate the impact of weight mapping schemes and device and peripheral circuitry non-idealities

Mitigating State-Drift in Memristor Crossbar Arrays for Vector Matrix Multiplication

In this Chapter, we review the recent progress on resistance drift mitigation techniques for resistive switching memory devices (specifically memristors) and its impact on the accuracy in deep neural network applications. In the first section of the chapter, we investigate the importance of soft errors and their detrimental impact on memristor-based vector–matrix multiplication (VMM) platforms performance specially the memristance state-drift induced by long-term recurring inference operations with sub-threshold stress voltage. Also, we briefly review some currently developed state-drift mitigation methods. In the next section of the chapter, we will discuss an adaptive inference technique with low hardware overhead to mitigate the memristance drift in memristive VMM platform by using optimization techniques to adjust the inference voltage characteristic associated with different network layers. Also, we present simulation results and performance improvements achieved by applying the proposed inference technique by considering non-idealities for various deep network applications on memristor crossbar arrays. This chapter suggests that a simple low overhead inference technique can revive the functionality, enhance the performance of memristor-based VMM arrays and significantly increases their lifetime which can be a very important factor toward making this technology as a main stream player in future in-memory computing platforms