Neuromorphic photonic circuit modeling in Verilog-A


Abstract "One of the significant challenges in neuromorphic photonic architectures is the lack of good tools to simulate large-scale photonic integrated circuits. It is crucial to perform simulations on a single platform to capture the circuit’s behavior in the presence of both optical and electrical components. Here, we adopted a Verilog-A based approach to model neuromorphic photonic cir... » read more

Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems


New academic paper from Washington State University, supported by a grant from the National Science Foundation. Abstract: "Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP),... » read more

Neuromorphic chip integrated with a large-scale integration circuit and amorphous-metal-oxide semiconductor thin-film synapse devices


New academic paper from Nara Institute of Science and Technology (NAIST) and Ryukoku University. Abstract "Artificial intelligences are promising in future societies, and neural networks are typical technologies with the advantages such as self-organization, self-learning, parallel distributed computing, and fault tolerance, but their size and power consumption are large. Neuromorphic syste... » read more

Research Bits: March 29


Brain-like AI chip Researchers from Purdue University, Santa Clara University, Portland State University, Pennsylvania State University, Argonne National Laboratory, University of Illinois Chicago, Brookhaven National Laboratory, and University of Georgia built a reprogrammable chip that could be used as the basis for brain-like AI hardware. “The brains of living beings can continuously l... » 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

Mapping Transformation Enabled High-Performance and Low-Energy Memristor-Based DNNs


Abstract: "When deep neural network (DNN) is extensively utilized for edge AI (Artificial Intelligence), for example, the Internet of things (IoT) and autonomous vehicles, it makes CMOS (Complementary Metal Oxide Semiconductor)-based conventional computers suffer from overly large computing loads. Memristor-based devices are emerging as an option to conduct computing in memory for DNNs to make... » read more

Seven Hardware Advances We Need to Enable The AI Revolution


The potential, positive impact AI will have on society at large is impossible to overestimate. Pervasive AI, however, remains a challenge. Training algorithms can take inordinate amounts of power, time, and computing capacity. Inference will also become more taxing with applications such as medical imaging and robotics. Applied Materials estimates that AI could consume up to 25% of global elect... » 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

An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks


Abstract:  "The development of brain-inspired neuromorphic computing architectures as a paradigm for Artificial Intelligence (AI) at the edge is a candidate solution that can meet strict energy and cost reduction constraints in the Internet of Things (IoT) application areas. Toward this goal, we present μBrain: the first digital yet fully event-driven without clock architecture, with co-lo... » read more

← Older posts Newer posts →