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Training Neural Networks for Execution on Approximate Hardware
Approximate computing methods have shown great potential for deep learning.
PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator
The last few years have seen a lot of work to address the challenge of low-latency and high-throughput convolutional neural network inference.
Bit-serial Weight Pools: Compression and Arbitrary Precision Execution of Neural Networks on Resource Constrained Processors
Applications of neural networks on edge systems have proliferated in recent years but the ever-increasing model size makes neural networks not able to deploy on resource-constrained microcontrollers efficiently.
High Throughput Multi-Channel Parallelized Diffraction Convolutional Neural Network Accelerator
Convolutional neural networks are paramount in image and signal processing including the relevant classification and training tasks alike and constitute for the majority of machine learning compute demand today.
Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network
Deep learning, accounting for the use of an elaborate neural network, has recently been developed as an efficient and powerful tool to solve diverse problems in physics and other sciences.
SWIS -- Shared Weight bIt Sparsity for Efficient Neural Network Acceleration
Quantization is spearheading the increase in performance and efficiency of neural network computing systems making headway into commodity hardware.
You May Not Need Order in Time Series Forecasting
Time series forecasting with limited data is a challenging yet critical task.
