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Found 7 papers, 2 papers with code
Support for Stock Trend Prediction Using Transformers and Sentiment Analysis
However, due to the limitations of RNNs, such as gradient vanish and long-term dependencies being lost as sequence length increases, in this paper we develop a Transformer based model that uses technical stock data and sentiment analysis to conduct accurate stock trend prediction over long time windows.
Stock Trend Prediction: A Semantic Segmentation Approach
However, semantic segmentation and its well-designed fully convolutional networks have never been studied for time-series dense classification.
A Two-Stage Efficient 3-D CNN Framework for EEG Based Emotion Recognition
This paper proposes a novel two-stage framework for emotion recognition using EEG data that outperforms state-of-the-art models while keeping the model size small and computationally efficient.
PLAM: a Posit Logarithm-Approximate Multiplier
The Posit Number System was introduced in 2017 as a replacement for floating-point numbers.
The Effects of Approximate Multiplication on Convolutional Neural Networks
The approximate multiplication can reduce the cost of the underlying circuits so that CNN inferences can be performed more efficiently in hardware accelerators.
Reliable and Energy Efficient MLC STT-RAM Buffer for CNN Accelerators
We propose a lightweight scheme where the formation of a data block is changed in such a way that it can tolerate soft errors significantly better than the baseline.
Partition Pruning: Parallelization-Aware Pruning for Deep Neural Networks
We evaluated the performance and energy consumption of parallel inference of partitioned models, which showed a 7. 72x speed up of performance and a 2. 73x reduction in the energy used for computing pruned layers of TinyVGG16 in comparison to running the unpruned model on a single accelerator.
