Results 51 to 60 of about 14,001 (185)

Reconfigurable In-Sensor Computing Memristor for Olfactory SNN and Reservoir Hybrid Neuromorphic Computing

Research
Traditional gas sensing systems are facing efficiency challenges due to physically separated von Neumann architectures, making the construction of in-sensor computing neuromorphic olfactory systems urgently needed for low-power and low-latency scenarios.
Lin Lu, Jinhao Zhang, Qingxin Chen, Jialin Meng, Yongjin Zou, Yilin Wang, Tianyu Wang   +6 more
doaj   +1 more source

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

, 2017
Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices.
Adiraju, Prathyusha, Catthoor, Francky, Das, Anup, Dutt, Nikil, Groenendaal, Willemijn, Krichmar, Jeffrey L., Pradhapan, Paruthi, Rajan, Raj Thilak, Schaafsma, Siebren, Van Hoof, Chris   +9 more
core   +1 more source

Six networks on a universal neuromorphic computing substrate [PDF]

, 2013
In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and ...
Andreas eGrübl, Daniel eBrüderle, Eric eMüller, Johannes eSchemmel, Karlheinz eMeier, Michael eSchmuker, Michael eSchmuker, Mihai A. ePetrovici, Paul eMüller, Sebastian eJeltsch, Thomas ePfeil   +10 more
core   +3 more sources

Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips

, 2017
Deep Neural Networks (DNN) achieve human level performance in many image analytics tasks but DNNs are mostly deployed to GPU platforms that consume a considerable amount of power.
Mashford, Benjamin Scott, Tang, Jianbin, Yepes, Antonio Jimeno   +2 more
core   +1 more source

A Low-Power High-Speed Spintronics-Based Neuromorphic Computing System Using Real-Time Tracking Method [PDF]

IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2018
In spintronic-based neuromorphic computing systems (NCSs), the switching of magnetic moment in a magnetic tunnel junction (MTJ) is used to mimic neuron firing. However, the stochastic switching behavior of the MTJ and process variations effect lead to a significant increase in the stimulation time of such NCSs.
Hooman Farkhani, Mohammad Tohidi, Sadaf Farkhani, Jens Kargaard Madsen, Farshad Moradi   +4 more
openaire   +1 more source

Van der Waals materials-based floating gate memory for neuromorphic computing

Chip, 2023
: With the advent of the “Big Data Era”, improving data storage density and computation speed has become more and more urgent due to the rapid growth in different types of data.
Qianyu Zhang, Zirui Zhang, Ce Li, Renjing Xu, Dongliang Yang, Linfeng Sun   +5 more
doaj   +1 more source

Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware

, 2016
In recent years the field of neuromorphic low-power systems that consume orders of magnitude less power gained significant momentum. However, their wider use is still hindered by the lack of algorithms that can harness the strengths of such architectures.
Cassidy, Andrew, Diehl, Peter U, IEEE, Neftci, Emre, Pedroni, Bruno U, Zarrella, Guido   +5 more
core   +1 more source

A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing

Nature Communications, 2023
Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts.
Han Xu, Dashan Shang, Qing Luo, Junjie An, Yue Li, Shuyu Wu, Zhihong Yao, Woyu Zhang, Xiaoxin Xu, Chunmeng Dou, Hao Jiang, Liyang Pan, Xumeng Zhang, Ming Wang, Zhongrui Wang, Jianshi Tang, Qi Liu, Ming Liu   +17 more
doaj   +1 more source

Memcapacitive Devices in Logic and Crossbar Applications [PDF]

, 2017
Over the last decade, memristive devices have been widely adopted in computing for various conventional and unconventional applications. While the integration density, memory property, and nonlinear characteristics have many benefits, reducing the energy
Teuscher, Christof, Tran, Dat
core   +2 more sources

Stochastic Memristive Devices for Computing and Neuromorphic Applications

, 2013
Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems.
Choi, Shinhyun, Gaba, Siddharth, Lu, Wei, Sheridan, Patrick, Zhou, Jiantao   +4 more
core   +1 more source