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Found 10 papers, 1 papers with code
CipherPrune: Efficient and Scalable Private Transformer Inference
Our experiments demonstrate that CipherPrune reduces the execution overhead of private Transformer inference by approximately $6. 1\times$ for 128-token inputs and $10. 6\times$ for 512-token inputs, compared to previous methods, with only a marginal drop in accuracy.
Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning
With this dataset, we developed HRV models that combine signal processing and ML to directly infer HRV.
PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models
Moreover, our analysis showed that DT models with 10 to 20 input features usually have good accuracy, while are several magnitude smaller in model sizes and faster in inference time.
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off
We further design an acquisition function and provide the theoretical guarantees for the proposed method and clarify its convergence property.
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System
Energy harvesting (EH) technology that harvests energy from ambient environment is a promising alternative to batteries for powering those devices due to the low maintenance cost and wide availability of the energy sources.
An Automatic and Efficient BERT Pruning for Edge AI Systems
On MRPC, we obtain a 4. 6 higher score than the SOTA at the same overall pruning ratio of 0. 5.
Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices
Energy harvesting (EH) IoT devices that operate intermittently without batteries, coupled with advances in deep neural networks (DNNs), have opened up new opportunities for enabling sustainable smart applications.
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit.
Binary Complex Neural Network Acceleration on FPGA
Deep complex networks (DCN), in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision-making requirements of many deployable systems dealing with short observations or short signal bursts.
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA
In natural language processing (NLP), the "Transformer" architecture was proposed as the first transduction model replying entirely on self-attention mechanisms without using sequence-aligned recurrent neural networks (RNNs) or convolution, and it achieved significant improvements for sequence to sequence tasks.
