Results 41 to 50 of about 41,575 (296)
Training results of DNN models and PCA-DNN models.
, 2021 Training results of DNN models and PCA-DNN models.
Kuo-Wei Chao (11874072) +5 more
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DNN-based uncertainty estimation for weighted DNN-HMM ASR
CoRR, 2017 In this paper, the uncertainty is defined as the mean square error between a given enhanced noisy observation vector and the corresponding clean one. Then, a DNN is trained by using enhanced noisy observation vectors as input and the uncertainty as output with a training database.
José Novoa +2 more
openaire +2 more sources
, 2023 In order to overcome the discreteness of input data and training data in deep neural network (DNN), the multivariable response surface function was used to revise input data and training data in this paper.
Xiaohong Chen (220164) +2 more
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A Formalism of DNN Accelerator Flexibility
ACM SIGMETRICS Performance Evaluation Review, 2022 The high efficiency of domain-specific hardware accelerators for machine learning (ML) has come fromspecialization, with the trade-off of less configurability/ flexibility. There is growing interest in developingflexible ML accelerators to make them future-proof to the rapid evolution of Deep Neural Networks (DNNs).
Sheng-Chun Kao +4 more
openaire +2 more sources
Res-DNN: A Residue Number System-Based DNN Accelerator Unit
IEEE Transactions on Circuits and Systems I: Regular Papers, 2020 In this article, a technique, based on using Residue Number System (RNS) is suggested to improve the energy efficiency of Deep Neural Networks (DNNs). In the DNN architecture, which is fully RNS-based, only weights and the primary inputs in the main memory are in the binary number system (BNS).
Nasim Samimi +3 more
openaire +1 more source
A Heterogeneous RISC-V Processor for Efficient DNN Application in Smart Sensing System
Sensors, 2021 Extracting features from sensing data on edge devices is a challenging application for which deep neural networks (DNN) have shown promising results. Unfortunately, the general micro-controller-class processors which are widely used in sensing system ...
Haifeng Zhang +7 more
doaj +1 more source
?????? DNN ???????????? ????????? ?????? ??????????????? ???????????? ?????? ?????? ?????? ?????? [PDF]
, 2022 Department of Computer Science and EngineeringDistributed Deep Neural Network(DNN) training is suffering the CPU bottleneck due to the heavy pre- processing computation.
Park, Chanho
core
SEMI-CUSTOM DESIGN OF MULTIPLY-ACCUMULATE UNIT [PDF]
Proceedings on Engineering SciencesIn this research work, a novel approach to design a low-power 16-bit Multiply-Accumulate (MAC) unit for deep neural network (DNN) accelerators is presented. The approach integrates the Karatsuba Algorithm, Vedic multiplier using Urdhva Tiryagbhyam Sutra,
Christopher C R , Umadevi S
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A Dnn-Ensemble Method for Error Reduction and Training Data Selection in Dnn based Modeling
, 2022 Deep neural networks (DNNs) have been widely adopted in modeling electromagnetic compatibility (EMC) problems, but the training data acquisition is usually time-consuming through various simulators.
Li, Da +9 more
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