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Mostrando ítems 1-10 de 11
Artículo
Liquid State Machine on SpiNNaker for Spatio-Temporal Classification Tasks
(Frontiers Media S.A., 2022-03-14)
Liquid State Machines (LSMs) are computing reservoirs composed of recurrently connected Spiking Neural Networks which have attracted research interest for their modeling capacity of biological structures and as promising ...
Ponencia
Experimental Body-input Three-stage DC offset Calibration Scheme for Memristive Crossbar
(IEEE, 2020)
Reading several ReRAMs simultaneously in a neuromorphic circuit increases power consumption and limits scalability. Applying small inference read pulses is a vain attempt when offset voltages of the read-out circuit are ...
Artículo
Enhanced Linearity in FD-SOI CMOS Body-Input Analog Circuits - Application to Voltage-Controlled Ring Oscillators and Frequency-Based sigma Delta ADCs
(Institute of Electrical and Electronics Engineers, 2020)
Abstract— This paper investigates the use of the body terminal of MOS transistors to improve the linearity of some key circuits used to implement analog and mixed-signal circuits integrated in Fully Depleted Silicon on ...
Artículo
CMOS Front End for Interfacing Spin-Hall Nano-Oscillators for Neuromorphic Computing in the GHz Range
(MDPI, 2023-01)
Spin-Hall-effect nano-oscillators are promising beyond the CMOS devices currently avail- able, and can potentially be used to emulate the functioning of neurons in computational neuromor- phic systems. As they oscillate ...
Ponencia
Implementation of binary stochastic STDP learning using chalcogenide-based memristive devices
(IEEE, 2021-04)
The emergence of nano-scale memristive devices encouraged many different research areas to exploit their use in multiple applications. One of the proposed applications was to implement synaptic connections in bio-inspired ...
Artículo
Effect of Device Mismatches in Differential Oscillatory Neural Networks
(IEEE, 2023-02)
Analog implementation of Oscillatory Neural Networks (ONNs) has the potential to implement fast and ultra-low-power computing capabilities. One of the drawbacks of analog implementation is component mismatches which cause ...
Artículo
Event-driven implementation of deep spiking convolutional neural networks for supervised classification using the SpiNNaker neuromorphic platform
(Elsevier, 2020)
Neural networks have enabled great advances in recent times due mainly to improved parallel computing capabilities in accordance to Moore’s Law, which allowed reducing the time needed for the parameter learning of complex, ...
Ponencia
Event data downscaling for embedded computer vision
(SciTePress, 2022-02)
Event cameras (or silicon retinas) represent a new kind of sensor that measure pixel-wise changes in brightness and output asynchronous events accordingly. This novel technology allows for a sparse and energy-efficient ...
Artículo
Neuromorphic Low-Power Inference on Memristive Crossbars With On-Chip Offset Calibration
(IEEE, 2021-03)
Monolithic integration of silicon with nano-sized Redox-based resistive Random-Access Memory (ReRAM) devices opened the door to the creation of dense synaptic connections for bio-inspired neuromorphic circuits. One ...
Tesis Doctoral
Memristor Based Event Driven Neuromorphic Nano-CMOS Processor
(2021-02-19)
‘Neuromorphic engineering’ has been showing significant developments in recent days. The word ‘neuromorphic’ was first coined by Caver Mead, which is morphing biological brain on-chip [1]. The main idea is to use the ...