TentacleNet: A Pseudo-Ensemble Template for Accurate Binary Convolutional Neural Networks

Mocerino, Luca; Calimera, Andrea

doi:10.1109/aicas48895.2020.9073982

Cited by 4 publications

(2 citation statements)

References 33 publications

(42 reference statements)

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“…TentacleNet [58] is also inspired by the ensemble learning theory. Compared with BENN [54], TentacleNet takes a step forward, showing that binary ensembles can reach high accuracy with fewer resources.…”

Section: G Optimizationmentioning

confidence: 99%

A Review of Recent Advances of Binary Neural Networks for Edge Computing

Zhao

Gong

et al. 2021

IEEE J. Miniat. Air Space Syst.

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Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains such as real-time unmanned aerial systems, industrial applications, and the demand for privacy protection. This paper reviews recent advances on binary neural network (BNN) and 1-bit CNN technologies that are well suitable for front-end, edge-based computing. We introduce and summarize existing work and classify them based on gradient approximation, quantization, architecture, loss functions, optimization method, and binary neural architecture search. We also introduce applications in the areas of computer vision and speech recognition and discuss future applications for edge computing.

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Section: G Optimizationmentioning

confidence: 99%

A Review of Recent Advances of Binary Neural Networks for Edge Computing

Zhao

Gong

et al. 2021

IEEE J. Miniat. Air Space Syst.

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show abstract

“…Arithmetic precision scaling is by far the most adopted strategy [11,12,30]. The parameters, trained and optimized using a floating-point (FP) representation, are rescaled to integers with a lower bit-width, e.g., 8-bit as the most common option.…”

Section: Convnets Approximation Via Arithmetic Approximation and Data-reusementioning

confidence: 99%

AxP: A HW-SW Co-Design Pipeline for Energy-Efficient Approximated ConvNets via Associative Matching

Mocerino

Calimera

2021

Applied Sciences

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The reduction in energy consumption is key for deep neural networks (DNNs) to ensure usability and reliability, whether they are deployed on low-power end-nodes with limited resources or high-performance platforms that serve large pools of users. Leveraging the over-parametrization shown by many DNN models, convolutional neural networks (ConvNets) in particular, energy efficiency can be improved substantially preserving the model accuracy. The solution proposed in this work exploits the intrinsic redundancy of ConvNets to maximize the reuse of partial arithmetic results during the inference stages. Specifically, the weight-set of a given ConvNet is discretized through a clustering procedure such that the largest possible number of inner multiplications fall into predefined bins; this allows an off-line computation of the most frequent results, which in turn can be stored locally and retrieved when needed during the forward pass. Such a reuse mechanism leads to remarkable energy savings with the aid of a custom processing element (PE) that integrates an associative memory with a standard floating-point unit (FPU). Moreover, the adoption of an approximate associative rule based on a partial bit-match increases the hit rate over the pre-computed results, maximizing the energy reduction even further. Results collected on a set of ConvNets trained for computer vision and speech processing tasks reveal that the proposed associative-based hw-sw co-design achieves up to 77% in energy savings with less than 1% in accuracy loss.

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Binary Neural Networks

Zhang,

Wang,

et al. 2024

Computational Intelligence Methods and Applications

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TentacleNet: A Pseudo-Ensemble Template for Accurate Binary Convolutional Neural Networks

Cited by 4 publications

References 33 publications

A Review of Recent Advances of Binary Neural Networks for Edge Computing

A Review of Recent Advances of Binary Neural Networks for Edge Computing

AxP: A HW-SW Co-Design Pipeline for Energy-Efficient Approximated ConvNets via Associative Matching

Binary Neural Networks

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