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Found 8 papers, 1 papers with code
YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration
Convolutional neural networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing image classification beyond human accuracy.
Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine
Deep neural networks have achieved impressive results in computer vision and machine learning.
XNORBIN: A 95 TOp/s/W Hardware Accelerator for Binary Convolutional Neural Networks
Deploying state-of-the-art CNNs requires power-hungry processors and off-chip memory.
Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management
Radio Resource Management (RRM) in 5G mobile communication is a challenging problem for which Recurrent Neural Networks (RNN) have shown promising results.
ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator
Furthermore, it can perform inference on a binarized ResNet-18 trained with 8-bases Group-Net to achieve a 67. 5% Top-1 accuracy with only 3. 0 mJ/frame -- at an accuracy drop of merely 1. 8% from the full-precision ResNet-18.
Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices
This BNN reaches a 77. 9% accuracy, just 7% lower than the full-precision version, with 58 kB (7. 2 times less) for the weights and 262 kB (2. 4 times less) memory in total.
Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile
The Winograd-enhanced DSA achieves up to 1. 85x gain in energy efficiency and up to 1. 83x end-to-end speed-up for state-of-the-art segmentation and detection networks.
Stella Nera: Achieving 161 TOp/s/W with Multiplier-free DNN Acceleration based on Approximate Matrix Multiplication
From classical HPC to deep learning, MatMul is at the heart of today's computing.
