Efficient embedded learning for IoT devices

Venkataramani, Swagath; Roy, Kaushik; Raghunathan, Anand

doi:10.1109/aspdac.2016.7428029

Cited by 31 publications

(17 citation statements)

References 23 publications

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“…The similarity between them is obvious, including the minimum at around 0.35 mHz and the maximum at 0.5 mHz. The most apparent difference appears in the higher frequencies, which can be attributed to both the error in number quantization [44] and the noise from a random walk [37] as expressed in Equations (1) and (2). Further investigation and analysis of the demonstrated chaotic behavior included calculation of correlation dimension, Kolmogorov entropy, as well as Lyapunov exponents.…”

Section: Chaotic Evaluationmentioning

confidence: 99%

“…All these technologies call for innovative approaches [2,3] and some of those approaches are approximate computing [4][5][6], deep learning [7], new post-CMOS devices and architectures [8], or advanced processing techniques [9,10]. One of the fields where more computational power is required is communications, especially when data privacy protection and security are included.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Computing Implementation of Chaotic Systems

2021

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An exploding demand for processing capabilities related to the emergence of the Internet of Things (IoT), Artificial Intelligence (AI), and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations—four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the Stochastic Computing (SC) framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications.

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Section: Chaotic Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic Computing Implementation of Chaotic Systems

2021

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“…around 3.5e-4 Hz and the maximum at 5e-3 Hz. The most apparent difference appears in the higher frequencies, which can be attributed to both the error in number quantization [44] and the noise from a random walk [37] as expressed in equations (1) and (2). Further investigation and analysis of the demonstrated chaotic behavior included calculation of correlation dimension, Kolmogorov entropy, as well as Lyapunov exponents.…”

Section: Chaotic Evaluationmentioning

confidence: 99%

Stochastic Computing Implementation of Chaotic Systems

Camps¹,

Stavrinides²,

Picos³

2021

Preprint

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An exploding demand for processing capabilities related to the emergence of the IoT, AI and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path, but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations; four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the SC framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications.

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“…Amidst the justified excitement about the success of artificial intelligence in man vs. machine contests such as IBM's Watson [7] and Google's AlphaGo [8], the gap in energy efficiency between artificial and natural intelligence continues to grow. Improved energy efficiency is crucial in the face of exploding computational requirements for training state-of-the-art ANNs on the one hand [9], and the need to deploy them in highly energy-constrained energy devices on the other hand [10]. Recent efforts also suggest that biologically inspired mechanisms also have the potential to improve the robustness of ANNs to adversarial attacks [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Brain Predictivity of Artificial Neural Networks with Nonlinear Response Mapping

Anand¹,

Sen

Roy

2020

Preprint

Self Cite

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Quantifying the similarity between artificial neural networks (ANNs) and their biological counterparts is an important step towards building more brain-like artificial intelligence systems. Recent efforts in this direction use neural predictivity, or the ability to predict the responses of a biological brain given the information in an ANN (such as its internal activations), when both are presented with the same stimulus. We propose a new approach to quantifying neural predictivity by explicitly mapping the activations of an ANN to brain responses with a nonlinear function, and measuring the error between the predicted and actual brain responses. Further, we propose to use a neural network to approximate this mapping function by training it on a set of neural recordings. The proposed method was implemented within the Tensorflow framework and evaluated on a suite of 8 state-of-the-art image recognition ANNs. Our experiments suggest that the use of a non-linear mapping function leads to higher neural predictivity. Our findings also reaffirm the observation that the latest advances in classification performance of image recognition ANNs are not matched by improvements in their neural predictivity. Finally, we examine the impact of pruning, a widely used ANN optimization, on neural predictivity, and demonstrate that network sparsity leads to higher neural predictivity.

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Efficient embedded learning for IoT devices

Cited by 31 publications

References 23 publications

Stochastic Computing Implementation of Chaotic Systems

Stochastic Computing Implementation of Chaotic Systems

Stochastic Computing Implementation of Chaotic Systems

Quantifying the Brain Predictivity of Artificial Neural Networks with Nonlinear Response Mapping

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