Ferroelectric and Negative Capacitance Devices

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• MEHMET CENGİZ ONBAŞLI

Using two phase-change memory devices per synapse, a three-layer perceptron network with 164 885 synapses is trained on a subset (5000 examples) of the MNIST database of handwritten digits using a backpropagation variant suitable for nonvolatile memory (NVM) + selector crossbar arrays, obtaining a training (generaliza…

Abstract In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights to the ana…

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomim…

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• NIMA JAFARI NAVIMIPOUR

We review our work towards achieving competitive performance (classification accuracies) for on-chip machine learning (ML) of large-scale artificial neural networks (ANN) using Non-Volatile Memory (NVM)-based synapses, despite the inherent random and deterministic imperfections of such devices. We then show that such…

A resistive RAM (ReRAM) macro is developed as a low-cost, magnetic-disturb-immune option for embedded, non-volatile memory for SoCs used in IoT and automotive applications. We demonstrate the smallest ReRAM subarray density of 10.1Mb/mm2in a 22nm low-power process. The subarray uses nominal-gate FINFET logic devices,…

Resistive random access memories (RRAMs) are favorable contenders in the race towards future technologies. Moreover, the desirable properties of memristor-based RRAM devices make them very good competitors in this field. The sneak paths problem poses one of the main difficulties in the construction of crossbar memory…

• FATİH GÜL

Parameter variations, which are increasing along with advances in process technologies, affect both timing and power. Variability must be considered at both the circuit and microarchitectural design levels to keep pace with performance scaling and to keep power consumption within reasonable limits. This article presen…

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has motivated the search for novel computing architectures targeti…

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• HASAN SÖZEN