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Found 11 papers, 1 papers with code
A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models
In recent years, the rapid evolution of computer vision has seen the emergence of various foundation models, each tailored to specific data types and tasks.
SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming
Deep learning (DL)-based solutions have emerged as promising candidates for beamforming in massive Multiple-Input Multiple-Output (mMIMO) systems.
Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming
First, we propose an energy model for different beamforming structures.
SAMSON: Sharpness-Aware Minimization Scaled by Outlier Normalization for Improving DNN Generalization and Robustness
In this work, we show that applying sharpness-aware training, by optimizing for both the loss value and loss sharpness, significantly improves robustness to noisy hardware at inference time without relying on any assumptions about the target hardware.
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming
Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems.
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.
RSSI-Based Hybrid Beamforming Design with Deep Learning
Hybrid beamforming is a promising technology for 5G millimetre-wave communications.
Layerwise Noise Maximisation to Train Low-Energy Deep Neural Networks
Deep neural networks (DNNs) depend on the storage of a large number of parameters, which consumes an important portion of the energy used during inference.
Training Modern Deep Neural Networks for Memory-Fault Robustness
Because deep neural networks (DNNs) rely on a large number of parameters and computations, their implementation in energy-constrained systems is challenging.
A Study of Deep Learning Robustness Against Computation Failures
For many types of integrated circuits, accepting larger failure rates in computations can be used to improve energy efficiency.
VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing
We also synthesize the circuits in a 65 nm CMOS technology and we show that the proposed integral stochastic architecture results in up to 21% reduction in energy consumption compared to the binary radix implementation at the same misclassification rate.
