Search results for "Neuromorphic engineering"
showing 10 items of 13 documents
Scaling up electrically synchronized spin torque oscillator networks
2018
AbstractSynchronized nonlinear oscillators networks are at the core of numerous families of applications including phased array wave generators and neuromorphic pattern matching systems. In these devices, stable synchronization between large numbers of nanoscale oscillators is a key issue that remains to be demonstrated. Here, we show experimentally that synchronized spin-torque oscillator networks can be scaled up. By increasing the number of synchronized oscillators up to eight, we obtain that the emitted power and the quality factor increase linearly with the number of oscillators. Even more importantly, we demonstrate that the stability of synchronization in time exceeds 1.6 millisecond…
Reinforcement learning in synthetic gene circuits.
2020
Synthetic gene circuits allow programming in DNA the expression of a phenotype at a given environmental condition. The recent integration of memory systems with gene circuits opens the door to their adaptation to new conditions and their re-programming. This lays the foundation to emulate neuromorphic behaviour and solve complex problems similarly to artificial neural networks. Cellular products such as DNA or proteins can be used to store memory in both digital and analog formats, allowing cells to be turned into living computing devices able to record information regarding their previous states. In particular, synthetic gene circuits with memory can be engineered into living systems to al…
Live demonstration: multiplexing AER asynchronous channels over LVDS Links with Flow-Control and Clock-Correction for Scalable Neuromorphic Systems
2017
Paper presented at the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), held in Baltimore, MD, USA, on 28-31 May 2017.
On Multiple AER Handshaking Channels Over High-Speed Bit-Serial Bidirectional LVDS Links With Flow-Control and Clock-Correction on Commercial FPGAs f…
2017
Address event representation (AER) is a widely employed asynchronous technique for interchanging “neural spikes” between different hardware elements in neuromorphic systems. Each neuron or cell in a chip or a system is assigned an address (or ID), which is typically communicated through a high-speed digital bus, thus time-multiplexing a high number of neural connections. Conventional AER links use parallel physical wires together with a pair of handshaking signals (request and acknowledge). In this paper, we present a fully serial implementation using bidirectional SATA connectors with a pair of low-voltage differential signaling (LVDS) wires for each direction. The proposed implementation …
Multiplexing AER asynchronous channels over LVDS links with flow-control and clock-correction for scalable neuromorphic systems
2017
Paper presented at the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), held in Baltimore, MD, USA, on 28-31 May 2017.
A magnetic skyrmion as a non-linear resistive element - a potential building block for reservoir computing
2017
Inspired by the human brain, there is a strong effort to find alternative models of information processing capable of imitating the high energy efficiency of neuromorphic information processing. One possible realization of cognitive computing are reservoir computing networks. These networks are built out of non-linear resistive elements which are recursively connected. We propose that a skyrmion network embedded in frustrated magnetic films may provide a suitable physical implementation for reservoir computing applications. The significant key ingredient of such a network is a two-terminal device with non-linear voltage characteristics originating from single-layer magnetoresistive effects,…
TiO2 in memristors and resistive random access memory devices
2021
Abstract One of the most recent applications of TiO2 thin films is as an oxide layer in memristors, electronic devices considered as one of the most promising nonvolatile memories and as possible building units for neuromorphic computing. This chapter aims to describe several fabrication ways, either (electro)chemical or physical methods, of TiO2 thin films and to highlight the relationship between method and layer properties. Some fundamentals on the mechanism of memristors’ operation, that is, resistive switching in oxide thin films, will be given, classifying the different types of devices based on the used electrode materials and underlying physicochemical processes. Finally, it will be…
Electrochemical Tantalum Oxide for Resistive Switching Memories
2017
Redox-based resistive switching memories (ReRAMs) are strongest candidates for the next-generation nonvolatile memories fulfilling the criteria for fast, energy efficient, and scalable green IT. These types of devices can also be used for selector elements, alternative logic circuits and computing, and memristive and neuromorphic operations. ReRAMs are composed of metal/solid electrolyte/metal junctions in which the solid electrolyte is typically a metal oxide or multilayer oxides structures. Here, this study offers an effective and cheap electrochemical approach to fabricate Ta/Ta2O5-based devices by anodizing. This method allows to grow high-quality and dense oxide thin films onto a metal…
Hardware-accelerated spike train generation for neuromorphic image and video processing
2014
Recent studies concerning Spiking Neural Networks show that they are a powerful tool for multiple applications as pattern recognition, image tracking, and detection tasks. The basic functional properties of SNN reside in the use of spike information encoding as the neurons are specifically designed and trained using spike trains. We present a novel and efficient frequency encoding algorithm with Gabor-like receptive fields using probabilistic methods and targeted to FPGA for online pro-cessing. The proposed encoding is versatile, modular and, when applied to images, it is able to perform simple image transforms as edge detection, spot detection or removal, and Gabor-like filtering without a…
Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network
2021
Abstract We investigate the constructive role of an external noise signal, in the form of a low-rate Poisson sequence of pulses supplied to all inputs of a spiking neural network, consisting in maintaining for a long time or even recovering a memory trace (engram) of the image without its direct renewal (or rewriting). In particular, this unique dynamic property is demonstrated in a single-layer spiking neural network consisting of simple integrate-and-fire neurons and memristive synaptic weights. This is carried out by preserving and even fine-tuning the conductance values of memristors in terms of dynamic plasticity, specifically spike-timing-dependent plasticity-type, driven by overlappi…