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- Privacy-preserving continual learning methods for medical image classification: a comparative analysis38
Continuous learning holds promise in mitigating catastrophic forgetting and facilitating continual model updates while preserving privacy in healthcare deep learning models, and presents a highly promising solution for the long-term clinical deployment of such models.
- Table-Lookup MAC: Scalable Processing of Quantised Neural Networks in FPGA Soft Logic11
This paper introduces Table Lookup Multiply-Accumulate (TLMAC) as a framework to compile and optimise quantised neural networks for scalable lookup-based processing and demonstrates that TLMAC significantly improves the scalability of previous related works.
- Is quantum optimization ready? An effort towards neural network compression using adiabatic quantum computing7
Experiments demonstrate that adiabatic quantum computing (AQC) not only outperforms classical algorithms like genetic algorithms and reinforcement learning in terms of time efficiency but also excels at identifying global optima.
- Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks7
A convolutional neural network model is developed for detecting atrial fibrillation from electrocardiogram (ECG) signals that demonstrates high performance despite being trained on limited, variable-length input data.
- Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems5
An efficient in-memory convolutional neural network (CNN) accelerator optimized for use with racetrack memory is presented and a series of fundamental arithmetic circuits as in-memory computing cells suited for multiply-and-accumulate operations are designed.
- Optimizing Neural Networks with Learnable Non-Linear Activation Functions via Lookup-Based FPGA Acceleration1
Evaluations using KANs demonstrate that the FPGA-based design achieves superior computational speed and over 104 times higher energy efficiency compared to edge CPUs and GPUs, while maintaining matching accuracy and minimal footprint overhead.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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