Mixed analog/digital matrix-vector multiplier for neural network synapses

In astronomy, the light emitted by an object travels through the vacuum of space and then the turbulent atmosphere before arriving at a ground based telescope. By passing through the atmosphere a series of turbulent layers modify the light's wave-front in such a way that Adaptive Optics reconstruction techniques are needed to improve the image quality. A novel reconstruction technique based in Artificial Neural Networks (ANN) is proposed. The network is designed to use the local tilts of the wave-front measured by a Shack Hartmann Wave-front Sensor (SHWFS) as inputs and estimate the turbulence in terms of Zernike coefficients. The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. The reconstructor was tested using three different atmospheric profiles and compared with two existing reconstruction techniques: Least Squares type Matrix Vector Multiplication (LS) and Learn and Apply (L + A).

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“…These include digital [32–34], analog [35,36], hybrid [37,38], FPGA based [39–41], and (non-electronic) optical implementations [42–44]. …”

Section: Future Implementation: Hardware Neural Networkmentioning

confidence: 99%

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

Juez

Lasheras

Roqueñí

et al. 2012

Sensors

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“…Categorized by storage types, there are five kinds of synapse circuits: capacitor only [1], [7]- [11], capacitor with refreshment [12]- [14], capacitor with EEPROM [4], digital [15], [16], and mixed D/A [17] circuits.…”

Section: B Synapse Circuitsmentioning

confidence: 99%

A CMOS feedforward neural-network chip with on-chip parallel learning for oscillation cancellation

Liu

Brooke

Hirotsu

2002

IEEE Trans. Neural Netw.

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Abstract-This paper presents a mixed signal CMOS feedforward neural-network chip with on-chip error-reduction hardware for real-time adaptation. The chip has compact on-chip weighs capable of high-speed parallel learning; the implemented learning algorithm is a genetic random search algorithm-the random weight change (RWC) algorithm. The algorithm does not require a known desired neural-network output for error calculation and is suitable for direct feedback control. With hardware experiments, we demonstrate that the RWC chip, as a direct feedback controller, successfully suppresses unstable oscillations modeling combustion engine instability in real time.Index Terms-Analog finite impulse response (FIR) filter, direct feedback control, neural-network chip, parallel on-chip learning, oscillation cancellation.

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“…Here are some issues that have been studied and explored in many researches. These include digital [3,4,14], analog [5,6], hybrid [7,8], FPGA based [9][10][11], and (non-electronic) optical implementations [12,13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Boolean Logic Using Ferroelectrics Capacitors as Basic Units of Artificial Neurons and Its Implementation in FPGA

Silva

Leite

McMillan

et al. 2015

Integrated Ferroelectrics

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In this work we propose the implementation of boolean logic through artificial neurons with Ferroelectric Capacitor (FeCapacitor) as its basic unit on a reconfigurable hardware platform. Two neurons were implemented: the Perceptron and the Spiking Neuron model. Both neurons use the phenomenon of the hysteresis loop as an activation function and were embedded on a Field Gate Programmable Gate Array (FPGA) hardware platform. The implementations were carried out by Simulink models and hardware synthesizable blocks from DSP Builder software and the results are shown in the form of the models and the boolean functions implemented by them.

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Mixed analog/digital matrix-vector multiplier for neural network synapses

Cited by 21 publications

References 19 publications

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

A CMOS feedforward neural-network chip with on-chip parallel learning for oscillation cancellation

Adaptive Boolean Logic Using Ferroelectrics Capacitors as Basic Units of Artificial Neurons and Its Implementation in FPGA

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