Reservoir Computing HW Based on a CMOS-Compatible FeFET

A new technical paper titled "Reservoir computing on a silicon platform with a ferroelectric field-effect transistor" was published by researchers at the University of Tokyo. Researchers report "reservoir computing hardware based on a ferroelectric field-effect transistor (FeFET) consisting of silicon and ferroelectric hafnium zirconium oxide. The rich dynamics originating from the ferroelec... » read more

Experimental photonic quantum memristor

Abstract "Memristive devices are a class of physical systems with history-dependent dynamics characterized by signature hysteresis loops in their input–output relations. In the past few decades, memristive devices have attracted enormous interest in electronics. This is because memristive dynamics is very pervasive in nanoscale devices, and has potentially groundbreaking applications ranging... » read more

Rotating neurons for all-analog implementation of cyclic reservoir computing

Abstract "Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly focused on the reservoir layer, whereas an end-to-end reservoir architecture has yet to be developed. Here, we propose a versatile method for implemen... » read more

Reservoir Computing based on Mutually Injected Phase Modulated Semiconductor Lasers as a Monolithic Integrated Hardware Accelerator

Abstract: "In this paper we propose and numerically study a neuromorphic computing scheme that applies delay-based reservoir computing in a laser system consisting of two mutually coupled phase modulated lasers. The scheme can be monolithic integrated in a straightforward manner and alleviates the need for external optical injection, as the data can be directly applied on the on-chip phase m... » read more

Next Generation Reservoir Computing

Abstract: "Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly, it requires very small training data sets, uses linear optimization, and thus requires minimal computing resources. However, the algorithm uses randomly sampled matrices to define the underlying recurrent neural n... » read more

Power/Performance Bits: Dec. 14

Improved digital sensing Researchers from Imperial College London and Technical University of Munich propose a technique to improve the capability of many different types of sensors. The method addresses voltage limits in analog-to-digital converters and the saturation that results in poor quality when an incoming signal exceeds those limits. “Our new technique lets us capture a fuller ra... » read more