Analyzing the effect of an integrate and fire encoder and decoder in feedback

Saxena, Shreya; Dahleh, Munther A.

doi:10.1109/cdc.2014.7039984

Cited by 3 publications

(3 citation statements)

References 18 publications

(9 reference statements)

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“…It can also help in reducing power consumption. It has been shown that single-channel time encoding has similar capabilities as traditional sampling: with time encoding, one can sample and reconstruct bandlimited signals [1,15,16] as well as signals with finite rate of innovation [17]. Here, we will show that time encoding also provides an advantage over classical sampling when it comes to multi-channel encoding.…”

Section: Introductionmentioning

confidence: 83%

Sampling and Reconstruction of Bandlimited Signals With Multi-Channel Time Encoding

Adam

Scholefield

Vetterli

2020

IEEE Trans. Signal Process.

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Sampling is classically performed by recording the amplitude of the input at given time instants; however, sampling and reconstructing a signal using multiple devices in parallel becomes a more difficult problem to solve when the devices have an unknown shift in their clocks.Alternatively, one can record the times at which a signal (or its integral) crosses given thresholds. This can model integrateand-fire neurons, for example, and has been studied by Lazar and Tóth under the name of "Time Encoding Machines". This sampling method is closer to what is found in nature.In this paper, we show that, when using time encoding machines, reconstruction from multiple channels has a more intuitive solution, and does not require the knowledge of the shifts between machines. We show that, if single-channel time encoding can sample and perfectly reconstruct a 2Ω-bandlimited signal, then M -channel time encoding can sample and perfectly reconstruct a signal with M times the bandwidth. Furthermore, we present an algorithm to perform this reconstruction and prove that it converges to the correct unique solution, in the noiseless case, without knowledge of the relative shifts between the machines. This is quite unlike classical multi-channel sampling, where unknown shifts between sampling devices pose a problem for perfect reconstruction.

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Section: Introductionmentioning

confidence: 83%

Sampling and Reconstruction of Bandlimited Signals With Multi-Channel Time Encoding

Adam

Scholefield

Vetterli

2020

IEEE Trans. Signal Process.

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“…Following these findings, studies were conducted to evaluate the performance of different reconstruction approaches that use results from uniform sampling [21]- [23], that use iterative reconstruction which is implementable in hardware [9,24], that run in an online fashion [25,26] or that apply to more general classes of signals [7].…”

Section: Introductionmentioning

confidence: 99%

Asynchrony Increases Efficiency: Time Encoding of Videos and Low-Rank Signals

Adam

Scholefield

Vetterli

2022

IEEE Trans. Signal Process.

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In event-based sensing, many sensors independently and asynchronously emit events when there is a change in their input. Event-based sensing can present significant improvements in power efficiency when compared to traditional sampling, because (1) the output is a stream of events where the important information lies in the timing of the events, and (2) the sensor can easily be controlled to output information only when interesting activity occurs at the input.Moreover, event-based sampling can often provide better resolution than standard uniform sampling. Not only does this occur because individual event-based sensors have higher temporal resolution [1], it also occurs because the asynchrony of events within a sensor and therefore across sensors allows for less redundant and more informative encoding. We would like to explain how such curious results come about.To do so, we use ideal time encoding machines as a proxy for event-based sensors. We explore time encoding of signals with low rank structure, and apply the resulting theory to video. We then see how the asynchronous firing across time encoding machines can couple spatial sampling density with temporal resolution, leading to better reconstruction, whereas, in frame-based video, temporal resolution depends solely on the frame-rate and spatial resolution solely on the pixel grid used.

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“…Many have suggested the use of the Integrate and Fire (IF) neuron [10]- [12] or the Leaky Integrate and Fire (LIF) neuron [13] directly as encoding stage, whether demonstrating their operation analytically, through software simulations, or proposing a CMOS design [14]. Zhao et al [15] propose a temporal encoder composed of a population of LIF neurons inside an iteration scheme, which features feedback spiking connections from the output back to the cluster of neurons.…”

Section: Introductionmentioning

confidence: 99%

Flexible Energy-Efficient Implementation of Adaptive Spiking Encoder for Neuromorphic Processors

Zamani

Ronchini

Huynh

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Neuromorphic computing could pave the way to a new generation of smart sensors that can process signals locally through Spiking Neural Networks (SNNs). For this paradigm to take hold, it is necessary to have an analog-to-spike encoder adaptable to a wide range of applications. The encoding system should offer the possibility to try different encoding algorithms, giving freedom to the designers to select the most appropriate approach for the target task. At the same time, it should feature a tunable parameter to modulate the spike density, in the pursuit of a compromise between accuracy and power consumption. Therefore, the goal of this work is to provide a platform enabling the conversion of analog signals to a sequence of spikes, characterized by flexibility, high energy efficiency, and small area. We introduce an encoder designed and simulated in a standard 0.18 μm CMOS process which benefits from a switchcapacitor and a dynamic comparator to achieve very high energy efficiency. The controller unit can switch between Slope-based orStep-Forward Encoding algorithms. The encoder consumes 30 fJ/spike at 1.5 V supply voltage and occupies an area of 0.00325 mm 2 .

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Analyzing the effect of an integrate and fire encoder and decoder in feedback

Cited by 3 publications

References 18 publications

Sampling and Reconstruction of Bandlimited Signals With Multi-Channel Time Encoding

Sampling and Reconstruction of Bandlimited Signals With Multi-Channel Time Encoding

Asynchrony Increases Efficiency: Time Encoding of Videos and Low-Rank Signals

Flexible Energy-Efficient Implementation of Adaptive Spiking Encoder for Neuromorphic Processors

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