65 research outputs found
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 200ÎĽmĂ—200ÎĽm
prototype was fabricated in a standard 0.35ÎĽm 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
Efficient Memristive Stochastic Differential Equation Solver
Herein, an efficient numerical solver for stochastic differential equations based on memristors is presented. The solver utilizes the stochastic switching effect in memristive devices to simulate the generation of a Brownian path and employs iterative Euler method computations within memristive crossbars. The correctness of the solution paths generated by the system is examined by solving the Black–Scholes equations and comparing the paths to analytical solutions. It is found that the absolute error of a 128-step path is limited to an order of (Figure presented.). The tolerance of the system to crossbar nonidealities is also assessed by comparing the numerical and analytical paths' variation in error. The numerical solver is sensitive to the variation in operating conditions, with the error increasing by (Figure presented.), (Figure presented.), and (Figure presented.) as the ambient temperature, wire resistance, and stuck probability of the memristor increase to extreme conditions. The solver is tested on a variety of problems to show its utility for different calculations. And, the resource consumption of the proposed structure built with existing technology is estimated and it is compared with similar iterative solvers. The solver generates a solution with the same level of accuracy from (Figure presented.) to (Figure presented.) faster than similar digital or mixed-signal designs
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
AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators
An adaptive inference method for crossbar (AIDX) is presented based on an
optimization scheme for adjusting the duration and amplitude of input voltage
pulses. AIDX minimizes the long-term effects of memristance drift on artificial
neural network accuracy. The sub-threshold behavior of memristor has been
modeled and verified by comparing with fabricated device data. The proposed
method has been evaluated by testing on different network structures and
applications, e.g., image reconstruction and classification tasks. The results
showed an average of 60% improvement in convolutional neural network (CNN)
performance on CIFAR10 dataset after 10000 inference operations as well as
78.6% error reduction in image reconstruction.Comment: This paper is submitted to IEEE Transactions Circuits and Systems II:
Express Brief
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
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