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Learning on JPEG-LDPC Compressed Images: Classifying with Syndromes
However, conventional entropic-coding methods like Huffman and Arithmetic break data structure, rendering them unsuitable for learning without decoding.
MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators
Memristors enable the computation of matrix-vector multiplications (MVM) in memory and, therefore, show great potential in highly increasing the energy efficiency of deep neural network (DNN) inference accelerators.
Decentralized Clustering on Compressed Data without Prior Knowledge of the Number of Clusters
Experimental results show that our two algorithms are competitive in terms of clustering performance with respect to K-means and DB-Scan, while lowering by a factor at least $2$ the amount of data exchanged between sensors.
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