3 papers
cs.AR2022
TCN-CUTIE: A 1036 TOp/s/W, 2.72 uJ/Inference, 12.2 mW All-Digital Ternary Accelerator in 22 nm FDX Technology
Moritz Scherer, Alfio Di Mauro, Tim Fischer +2
Tiny Machine Learning (TinyML) applications impose uJ/Inference constraints, with a maximum power consumption of tens of mW. It is extremely challenging to meet these requirements…
cs.AR2020
CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration with Better-than-Binary Energy Efficiency
Moritz Scherer, Georg Rutishauser, Lukas Cavigelli +1
We present a 3.1 POp/s/W fully digital hardware accelerator for ternary neural networks. CUTIE, the Completely Unrolled Ternary Inference Engine, focuses on minimizing non-computat…
cs.CV2019
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…