Shahin Hashemkhani

Electrically Reconfigurable Floating Gate Optoelectronic Synaptic Pixels for In-sensor Convolutional Image Feature Extraction with Built-in Contrast Enhancement

Md Sazzadur Rahman, Shahin Hashemkhani, Arijit Sarkar, J T Chen et autres

Image feature extraction and enhancement are fundamental operations in real-time object detection using convolutional neural networks (CNNs). In conventional architectures, continuous data transfer between sensors, memory, and processing units leads to high energy consumption and latency. In-pixel computing using optoelectronic synaptic (OS) …

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Artificial synapse with tunable dynamic range for neuromorphic computing with ion intercalated bilayer graphene

Yuzhi He, Shahin Hashemkhani, Yihan Liu, Daniel Vaz et autres

In neuromorphic computing, a tunable dynamic range in artificial synapses is crucial, as it allows devices to emulate the human brain’s efficiency in processing complex information with analog programmable states. Here, we introduce an electrochemical random-access memory (ECRAM) based on bilayer graphene. …

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Artificial Synapse with Tunable Dynamic Range for Neuromorphic Computing with Ion Intercalated Bilayer Graphene

Yuzhi He, Shahin Hashemkhani, Yihan Liu, Daniel Vaz et autres

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Toward autonomous event-based sensorimotor control with supervised gait learning and obstacle avoidance for robot navigation

Shahin Hashemkhani, Vijay Shankaran Vivekanand, Samarth Chopra, Rajkumar Kubendran

Miniature robots are useful during disaster response and accessing remote or unsafe areas. They need to navigate uneven terrains without supervision and under severe resource constraints such as limited compute, storage and power budget. Event-based sensorimotor control in edge robotics has potential …

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BioNN: Bio-Mimetic Neural Networks on Hardware Using Nonlinear Multi-Timescale Mixed-Feedback Control for Neuromodulatory Bursting Rhythms

K. Liu, Shahin Hashemkhani, Jonathan E. Rubin, Rajkumar Kubendran

Biological neurons exhibit rich and complex nonlinear dynamics, which are computationally expensive and area/power hungry for hardware implementation. This paper presents a mathematical analysis and hardware realization of neural networks using a nonlinear neuron model that utilizes two excitable systems operating at …

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Robot Locomotion through Tunable Bursting Rhythms using Efficient Bio-mimetic Neural Networks on Loihi and Arduino Platforms

Vijay Shankaran Vivekanand, Samarth Chopra, Shahin Hashemkhani, Rajkumar Kubendran

Rhythmic tasks that biological beings perform such as breathing, walking, and swimming, use specialized neural networks called central pattern generators (CPG). Spiking CPGs have already been implemented to control robot locomotion. This paper aims to take this concept further by designing and …

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Neuromorphic Networks using Nonlinear Mixed-feedback Multi-timescale Bio-mimetic Neurons

Shahin Hashemkhani, Jonathan E. Rubin, Rajkumar Kubendran

Biological neurons exhibit rich and complex nonlinear dynamics, which are computationally expensive and power-hungry for hardware implementation. This paper demonstrates the design and development of a hardware-friendly nonlinear neuron model based on an intuitive control theory perspective. The neuron consists of a …

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Robot Locomotion Control Using Central Pattern Generator with Non-linear Bio-mimetic Neurons

Vijay Shankaran Vivekanand, Shahin Hashemkhani, Shanmuga Venkatachalam, Rajkumar Kubendran

Central pattern generators (CPG) generate rhythmic gait patterns that can be tuned to exhibit various locomotion behaviors like walking, trotting, etc. CPGs inspired by biology have been implemented previously in robotics to generate periodic motion patterns. This paper aims to take the …

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Memtransistor Devices Based on MoS2 Multilayers with Volatile Switching due to Ag Cation Migration (Adv. Electron. Mater. 8/2022)

Matteo Farronato, Margherita Melegari, Saverio Ricci, Shahin Hashemkhani et autres

Memtransistor Devices The cover is an artist view of the mechanisms of resistance switching in a memtransistor device. Under an in-plane electric field, Ag cations migrate from one electrode to the other, eventually creating a bridge which is responsible for the macroscopic …

A CMOS–memristor hybrid system for implementing stochastic binary spike timing-dependent plasticity

Javad Ahmadi-Farsani, Saverio Ricci, Shahin Hashemkhani, Daniele Ielmini et autres

This paper describes a fully experimental hybrid system in which a [Formula: see text] memristive crossbar spiking neural network (SNN) was assembled using custom high-resistance state memristors with analogue CMOS neurons fabricated in 180 nm CMOS technology. The custom memristors used NMOS …

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A Hybrid Memristor/CMOS SNN for Implementing One-Shot Winner-Takes-All Training

Javad Ahmadi-Farsani, Saverio Ricci, Shahin Hashemkhani, Daniele Ielmini et autres

This paper presents a spiking neural network for pattern recognition. The network synapses are realized by resistive switching random access memory (ReRAM) cells, which are a stack of Au/Ti/C/Ti/HfO2/Pt. These cells are connected to an array of NMOS transistors (fabricated in a …

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Forming‐Free Resistive Switching Memory Crosspoint Arrays for In‐Memory Machine Learning

Saverio Ricci, Piergiulio Mannocci, Matteo Farronato, Shahin Hashemkhani et autres

In‐memory computing (IMC) with crosspoint arrays of resistive switching memory (RRAM) has gained wide attention for accelerating machine learning, data analysis, and deep neural networks. By IMC, matrix‐vector multiplication (MVM) can be executed in the crosspoint array in just one step, thus …

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