Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

Digital and analog TFET circuits: Design and benchmark

In this work, we investigate by means of simulations the performance of basic digital, analog, and mixed-signal circuits employing tunnel-FETs (TFETs). The analysis reviews and complements our previous papers on these topics. By considering the same devices for all the analysis, we are able to draw consistent conclusions for a wide variety of circuits. A virtual complementary TFET technology consisting of III-V heterojunction nanowires is considered. Technology Computer Aided Design (TCAD) models are calibrated against the results of advanced full-quantum simulation tools and then used to generate look-up-tables suited for circuit simulations. The virtual complementary TFET technology is benchmarked against predictive technology models (PTM) of complementary silicon FinFETs for the 10 nm node over a wide range of supply voltages (VDD) in the sub-threshold voltage domain considering the same footprint between the vertical TFETs and the lateral FinFETs and the same static power. In spite of the asymmetry between p- and n-type transistors, the results show clear advantages of TFET technology over FinFET for VDDlower than 0.4 V. Moreover, we highlight how differences in the I-V characteristics of FinFETs and TFETs suggest to adapt the circuit topologies used to implement basic digital and analog blocks with respect to the most common CMOS solutions

Digital and analog TFET circuits: Design and benchmark

In this work, we investigate by means of simulations the performance of basic digital, analog, and mixed-signal circuits employing tunnel-FETs (TFETs). The analysis reviews and complements our previous papers on these topics. By considering the same devices for all the analysis, we are able to draw consistent conclusions for a wide variety of circuits. A virtual complementary TFET technology consisting of III-V heterojunction nanowires is considered. Technology Computer Aided Design (TCAD) models are calibrated against the results of advanced full-quantum simulation tools and then used to generate look-up-tables suited for circuit simulations. The virtual complementary TFET technology is benchmarked against predictive technology models (PTM) of complementary silicon FinFETs for the 10 nm node over a wide range of supply voltages (VDD) in the sub-threshold voltage domain considering the same footprint between the vertical TFETs and the lateral FinFETs and the same static power. In spite of the asymmetry between p- and n-type transistors, the results show clear advantages of TFET technology over FinFET for VDDlower than 0.4 V. Moreover, we highlight how differences in the I-V characteristics of FinFETs and TFETs suggest to adapt the circuit topologies used to implement basic digital and analog blocks with respect to the most common CMOS solutions