A Uniform Modeling Methodology for Benchmarking DNN Accelerators

Palit, Indranil; Lou, Qiuwen; Perricone, Robert; Niemier, Michael; Hu, Xiaobo Sharon

doi:10.1109/iccad45719.2019.8942095

Cited by 10 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TANGO [44] employs those metrics to assess CNN models deployed on several hardware platforms. The importance of energy usage is emphasized by Palit et al [62], who present an energy estimation model along with empirical data from well-established CNNs. DNNTune [78] uses inference time and energy consumption to tune both CNNs and quantized networks for several application scenarios.…”

Section: Deep Neural Network Benchmark Analysismentioning

confidence: 99%

Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments

et al. 2022

View full text Add to dashboard Cite

Visual place recognition (VPR) is a robot's ability to determine whether a place was visited before using visual data. While conventional handcrafted methods for VPR fail under extreme environmental appearance changes, those based on convolutional neural networks (CNNs) achieve state-of-the-art performance but result in heavy runtime processes and model sizes that demand a large amount of memory. Hence, CNN-based approaches are unsuitable for resource-constrained platforms, such as small robots and drones. In this article, we take a multistep approach of decreasing the precision of model parameters, combining it with network depth reduction and fewer neurons in the classifier stage to propose a new class of highly compact models that drastically reduces the memory requirements and computational effort while maintaining state-of-the-art VPR performance. To the best of our knowledge, this is the first attempt to propose binary neural networks for solving the VPR problem effectively under changing conditions and with significantly reduced resource requirements. Our best-performing binary neural network, dubbed FloppyNet, achieves comparable VPR performance when considered against its full-precision and deeper counterparts while consuming 99% less memory and increasing the inference speed by seven times.

show abstract

Section: Deep Neural Network Benchmark Analysismentioning

confidence: 99%

Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments

et al. 2022

View full text Add to dashboard Cite

show abstract

“…where: 𝑙is the function; 𝛾 ∈ (0,1) is the factor and 𝜏 ∈ [0,1)is the threshold. Since the operation of lowering dropout probability with the predefined factor 𝛾 is differentiable, we can still optimize the opponent and the network-optimizer through (8) and (9). The compression process will stop when the percentage of left number of parameters in 𝐹 𝑊 (𝑥|𝑧) is smaller than a user-defined value 𝛼 ∈ (0,1).…”

Section: Network Compressing Routinementioning

confidence: 99%

“… memory capacity: neural networks achieve a high performance when using large number of neurons, which in turn requires large memory consumption to hold and process the model [8,9], [10]. As a result, compression could lower the memory requirements.…”

Section: Introduction Formulation Of the Problemmentioning

confidence: 99%

Methodology of neural network compression for multi-sensor transducernetwork models based on edge computing principles

Lobachev¹,

Antoshchuk²,

Hodovychenko³

2021

HAIT

View full text Add to dashboard Cite

This paper focuses on the development of a methodology to compress neural networks thatis based on the mechanism of prun-ingthe hidden layer neurons. The aforementioned neural networks are created in order to process the data generated by numerous sensors present in a transducer network that would be employed in a smart building. The proposed methodology implements a single approach for the compression of both Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) that are used for the tasks of classification and regression. The main principle behind this method is based on the dropout mechanism, which is employed as a regulation mechanism for the neural networks. The idea behind the method proposed consists of selecting optimal exclusion probability of a hidden layer neuron, based on the redundancy of the said neuron. The novelty of this method is theusage of a custom compression network thatis based on an RNN, which allows us to determine the redundancy parameter not just in a sin-gle hidden layer, but across severallayers. The additional novelty aspect consists of an iterative optimization of the network-optimizer, to have continuous improvement of the redundancy parameter calculator of the input network. For the experimental evalu-ation of the proposed methodology, the task of image recognition with a low-resolution camera was chosen, the CIFAR10 dataset was used to emulate the scenario. The VGGNet Convolutional Neural Network, that contains convolutional and fully connected lay-ers, was used as the network under test for the purposes of this experiment. The following two methods were taken as the analogous state of the art, the MagBase method, which is based on the sparcification principle as well as the method which is based on rarefied representation by employing the approach of rarefied encoding SFAC. The results of the experiment demonstrated that the amount of parameters in the compressed model is only 2.56% of the original input model. This has allowed us to reduce the logical output time by 93.7% and energy consumption by 94.8%. The proposed method allows to effectively usingdeep neural networks in transducer networks that utilize the architecture of edge computing. This in turn allows the system to process the data in real time, reduce the energy consumption and logical output time as well as lower the memory and storage requirements of real-world applications.

show abstract

“…TANGO [35] employs the metrics of inference time, power consumption and memory usage to access CNN models implemented on a variety of hardware platforms including an embedded GPU and a FPGA. Palit et al [36] highlight the importance of energy usage by presenting an energy estimation model and empirical data from several CNNs.…”

Section: B Benchmarkingmentioning

confidence: 99%

A Benchmark Comparison of Visual Place Recognition Techniques for Resource-Constrained Embedded Platforms

Power,

Zaffar,

Ferrarini

et al. 2021

Preprint

View full text Add to dashboard Cite

Visual Place Recognition (VPR) has been a subject of significant research over the last 15 to 20 years. VPR is a fundamental task for autonomous navigation as it enables self-localization within an environment. Although robots are often equipped with resource-constrained hardware, the computational requirements of and effects on VPR techniques have received little attention. In this work, we present a hardwarefocused benchmark evaluation of a number of state-of-the-art VPR techniques on public datasets. We consider popular single board computers, including ODroid, UP and Raspberry Pi 3, in addition to a commodity desktop and laptop for reference. We present our analysis based on several key metrics, including place-matching accuracy, image encoding time, descriptor matching time and memory needs. Key questions addressed include: (1) How does the performance accuracy of a VPR technique change with processor architecture? (2) How does power consumption vary for different VPR techniques and embedded platforms? (3) How much does descriptor size matter in comparison to today's embedded platforms' storage? (4) How does the performance of a high-end platform relate to an on-board low-end embedded platform for VPR? The extensive analysis and results in this work serve not only as a benchmark for the VPR community, but also provide useful insights for real-world adoption of VPR applications.

show abstract

A Uniform Modeling Methodology for Benchmarking DNN Accelerators

Cited by 10 publications

References 17 publications

Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments

Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments

Methodology of neural network compression for multi-sensor transducernetwork models based on edge computing principles

A Benchmark Comparison of Visual Place Recognition Techniques for Resource-Constrained Embedded Platforms

Contact Info

Product

Resources

About