A highly tunable 65-nm CMOS LIF neuron for a large scale neuromorphic system

2016 ◽  
Author(s):  
Syed Ahmed Aamir ◽  
Paul Muller ◽  
Andreas Hartel ◽  
Johannes Schemmel ◽  
Karlheinz Meier
Keyword(s):  
Large Scale ◽  
Neuromorphic System ◽  
Lif Neuron

A highly tunable 65-nm CMOS LIF neuron for a large scale neuromorphic system

2016 ◽  
Author(s):  
Syed Ahmed Aamir ◽  
Paul Muller ◽  
Andreas Hartel ◽  
Johannes Schemmel ◽  
Karlheinz Meier
Keyword(s):  
Large Scale ◽  
Neuromorphic System ◽  
Lif Neuron

scholarly journals Synaptic memory devices from CoO/Nb:SrTiO 3 junction

2019 ◽  
Vol 6 (4) ◽  
pp. 181098 ◽  
Author(s):  
Le Zhao ◽  
Jie Xu ◽  
Xiantao Shang ◽  
Xue Li ◽  
Qiang Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Charge Trapping ◽  
Spike Timing ◽  
Hebbian Learning Rule ◽  
Synaptic Functions ◽  
Neuromorphic System ◽  
Reduce Energy Consumption ◽  
Novel Design

Non-volatile memristors are promising for future hardware-based neurocomputation application because they are capable of emulating biological synaptic functions. Various material strategies have been studied to pursue better device performance, such as lower energy cost, better biological plausibility, etc. In this work, we show a novel design for non-volatile memristor based on CoO/Nb:SrTiO 3 heterojunction. We found the memristor intrinsically exhibited resistivity switching behaviours, which can be ascribed to the migration of oxygen vacancies and charge trapping and detrapping at the heterojunction interface. The carrier trapping/detrapping level can be finely adjusted by regulating voltage amplitudes. Gradual conductance modulation can therefore be realized by using proper voltage pulse stimulations. And the spike-timing-dependent plasticity, an important Hebbian learning rule, has been implemented in the device. Our results indicate the possibility of achieving artificial synapses with CoO/Nb:SrTiO 3 heterojunction. Compared with filamentary type of the synaptic device, our device has the potential to reduce energy consumption, realize large-scale neuromorphic system and work more reliably, since no structural distortion occurs.

Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks

2019 ◽  
Vol 49 (7) ◽  
pp. 2490-2503 ◽  
Author(s):  
Shuangming Yang ◽  
Jiang Wang ◽  
Bin Deng ◽  
Chen Liu ◽  
Huiyan Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Large Scale ◽  
Spiking Neural Networks ◽  
Neuromorphic System

scholarly journals Quantum Tunnelling based Ultra-compact and Energy Efficient Spiking Neuron enables Hardware Liquid State Machine

2021 ◽  
Author(s):  
Ajay Singh ◽  
Vivek Saraswat ◽  
Maryam Shojaei Baghini ◽  
Udayan Ganguly
Keyword(s):  
Low Power ◽  
Large Scale ◽  
Hardware Implementation ◽  
Cmos Technology ◽  
Quantum Tunnelling ◽  
Low Area ◽  
Standby Power ◽  
Similar Area ◽  
First Time ◽  
Lif Neuron

Abstract Low-power and low-area neurons are essential for hardware implementation of large-scale SNNs. Various novel physics based leaky-integrate-and-fire (LIF) neuron architectures have been proposed with low power and area, but are not compatible with CMOS technology to enable brain scale implementation of SNN. In this paper, for the first time, we demonstrate hardware implementation of LSM reservoir using band-to-band-tunnelling (BTBT) based neuron. A low-power thresholding circuit and current-to-voltage converter design are proposed. We further propose a predistortion technique to linearize a nonlinear neuron without any area and power overhead. We establish the equivalence of the proposed neuron with the ideal LIF neuron to demonstrate its versatility. To verify the effect of the proposed neuron, a 36-neuron LSM reservoir is fabricated in GF-45nm PDSOI technology. We achieved 5000x lower energy-per-spike at a similar area, 50x less area at a similar energy-per-spike, and 10x lower standby power at a similar area and energy-per-spike. Such overall performance improvement enables brain scale computing.

scholarly journals An ultra-compact leaky integrate-and-fire neuron with long and tunable time constant utilizing pseudo resistors for spiking neural networks

2021 ◽  
Author(s):  
Xiangyu Chen ◽  
Takeaki Yajima ◽  
Isao H. Inoue ◽  
Tetsuya Iizuka
Keyword(s):  
Neural Networks ◽  
Time Constant ◽  
Biological Networks ◽  
Large Scale ◽  
Cmos Technology ◽  
Spiking Neural Networks ◽  
Neuron Circuit ◽  
Area Efficiency ◽  
Integrate And Fire ◽  
Lif Neuron

Abstract Spiking neural networks (SNNs) inspired by biological neurons enable a more realistic mimicry of the human brain. To realize SNNs similar to large-scale biological networks, neuron circuits with high area efficiency are essential. In this paper, we propose a compact leaky integrate-and-fire (LIF) neuron circuit with a long and tunable time constant, which consists of a capacitor and two pseudo resistors (PRs). The prototype chip was fabricated with TSMC 65 nm CMOS technology, and it occupies a die area of 1392 m2. The fabricated LIF neuron has a power consumption of 6 W and a leak time constant of up to 1.2 ms (the resistance of PR is up to 600 MΩ). In addition, the time constants are tunable by changing the bias voltage of PRs. Overall, this proposed neuron circuit facilitates the very-large-scale integration (VLSI) of adaptive SNNs, which is crucial for the implementation of bio-scale brain-inspired computing.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Evolution of Large-Scale Coronal Structures

1994 ◽  
Vol 144 ◽  
pp. 29-33
Author(s):  
P. Ambrož
Keyword(s):  
Magnetic Field ◽  
Field Line ◽  
Large Scale ◽  
Coronal Magnetic Field ◽  
Source Surface ◽  
Solar Cycles ◽  
Characteristic Relationship ◽  
The Magnetic Field ◽  
Coronal Structures

AbstractThe large-scale coronal structures observed during the sporadically visible solar eclipses were compared with the numerically extrapolated field-line structures of coronal magnetic field. A characteristic relationship between the observed structures of coronal plasma and the magnetic field line configurations was determined. The long-term evolution of large scale coronal structures inferred from photospheric magnetic observations in the course of 11- and 22-year solar cycles is described.Some known parameters, such as the source surface radius, or coronal rotation rate are discussed and actually interpreted. A relation between the large-scale photospheric magnetic field evolution and the coronal structure rearrangement is demonstrated.

Sign in / Sign up

Export Citation Format

Share Document