39 citations · 84 across the 23 of their papers we have counts for
6 papers · 1 filter
RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing
Antoine Scardigli, Lukas Cavigelli, Lorenz K. Müller
Monte-Carlo path tracing is a powerful technique for realistic image synthesis but suffers from high levels of noise at low sample counts, limiting its use in real-time application…
RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks
Lukas Cavigelli, Luca Benini
We present Random Partition Relaxation (RPR), a method for strong quantization of neural networks weight to binary (+1/-1) and ternary (+1/0/-1) values. Starting from a pre-trained…
EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators
Lukas Cavigelli, Georg Rutishauser, Luca Benini
In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive…
Extended Bit-Plane Compression for Convolutional Neural Network Accelerators
Lukas Cavigelli, Luca Benini
After the tremendous success of convolutional neural networks in image classification, object detection, speech recognition, etc., there is now rising demand for deployment of thes…
CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams
Lukas Cavigelli, Luca Benini
The last few years have brought advances in computer vision at an amazing pace, grounded on new findings in deep neural network construction and training as well as the availabilit…
XNORBIN: A 95 TOp/s/W Hardware Accelerator for Binary Convolutional Neural Networks
Andrawes Al Bahou, Geethan Karunaratne, Renzo Andri +2
Deploying state-of-the-art CNNs requires power-hungry processors and off-chip memory. This precludes the implementation of CNNs in low-power embedded systems. Recent research shows…