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- Nazari, N., et al.
(författare)
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Multi-level Binarized LSTM in EEG Classification for Wearable Devices
- 2020
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Ingår i: Proceedings - 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2020. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728165820 ; , s. 175-181
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Konferensbidrag (refereegranskat)abstract
- Long Short-Term Memory (LSTM) is widely used in various sequential applications. Complex LSTMs could be hardly deployed on wearable and resourced-limited devices due to the huge amount of computations and memory requirements. Binary LSTMs are introduced to cope with this problem, however, they lead to significant accuracy loss in some applications such as EEG classification which is essential to be deployed in wearable devices. In this paper, we propose an efficient multi-level binarized LSTM which has significantly reduced computations whereas ensuring an accuracy pretty close to full precision LSTM. By deploying 5-level binarized weights and inputs, our method reduces area and delay of MAC operation about 31× and 27× in 65nm technology, respectively with less than 0.01% accuracy loss. In contrast to many compute-intensive deep-learning approaches, the proposed algorithm is lightweight, and therefore, brings performance efficiency with accurate LSTM-based EEG classification to realtime wearable devices.
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- Sinaei, Sima, et al.
(författare)
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Ada in real-time embedded system
- 2013
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Ingår i: Research Journal of Applied Sciences, Engineering and Technology. - : Maxwell Science Publications. - 2040-7459 .- 2040-7467. ; 5:14, s. 3803-3809
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Tidskriftsartikel (refereegranskat)abstract
- Ada has an important role in the real-time/embedded/safety-critical areas. It is the only ISO-standard, object-oriented, concurrent, real-time programming language. Ada is used as a usual language for application areas such as defense embedded systems that reliability and efficiency are very essential. One of the main Ada’s characteristics in compare with other programming languages is that, Ada was developed from the ground up with capabilities that provide real-time requirements. In this study it will be shown why Ada is used as the new standard for real-time programming languages and basic characteristics of real-time programming system in general and how they are addressed in Ada will be explained.
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- Sinaei, Sima, et al.
(författare)
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ELC-ECG : Efficient LSTM cell for ECG classification based on quantized architecture
- 2021
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Ingår i: Proceedings - IEEE International Symposium on Circuits and Systems. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728192017 ; May
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Konferensbidrag (refereegranskat)abstract
- Long Short-Term Memory (LSTM) is one of the most popular and effective Recurrent Neural Network (RNN) models used for sequence learning in applications such as ECG signal classification. Complex LSTMs could hardly be deployed on resource-limited bio-medical wearable devices due to the huge amount of computations and memory requirements. Binary LSTMs are introduced to cope with this problem. However, naive binarization leads to significant accuracy loss in ECG classification. In this paper, we propose an efficient LSTM cell along with a novel hardware architecture for ECG classification. By deploying 5-level binarized inputs and just 1-level binarization for weights, output, and in-memory cell activations, the delay of one LSTM cell operation is reduced 50x with about 0.004% accuracy loss in comparison with full precision design of ECG classification.
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- Sinaei, Sima, et al.
(författare)
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MuBiNN : Multi-level binarized recurrent neural network for EEG signal classification
- 2020
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Ingår i: Proceedings - IEEE International Symposium on Circuits and Systems. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728133201 ; October
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Konferensbidrag (refereegranskat)abstract
- Recurrent Neural Networks (RNN) are widely used for learning sequences in applications such as EEG classification. Complex RNNs could be hardly deployed on wearable devices due to their computation and memory-intensive processing patterns. Generally, reduction in precision leads much more efficiency and binarized RNNs are introduced as energy-efficient solutions. However, naive binarization methods lead to significant accuracy loss in EEG classification. In this paper, we propose a multi-level binarized LSTM, which significantly reduces computations whereas ensuring an accuracy pretty close to the full precision LSTM. Our method reduces the delay of the 3-bit LSTM cell operation 47× with less than 0.01% accuracy loss.
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