S
Shih-Chii Liu
Researcher at University of Zurich
Publications - 218
Citations - 12129
Shih-Chii Liu is an academic researcher from University of Zurich. The author has contributed to research in topics: Neuromorphic engineering & Artificial neural network. The author has an hindex of 42, co-authored 217 publications receiving 9291 citations. Previous affiliations of Shih-Chii Liu include Spanish National Research Council & ETH Zurich.
Papers
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Journal ArticleDOI
Neuromorphic Silicon Neuron Circuits
Giacomo Indiveri,Bernabe Linares-Barranco,Tara Julia Hamilton,André van Schaik,Ralph Etienne-Cummings,Tobi Delbruck,Shih-Chii Liu,Piotr Dudek,Philipp Hafliger,Sylvie Renaud,Johannes Schemmel,Gert Cauwenberghs,John V. Arthur,Kai Hynna,Fopefolu Folowosele,Sylvain Saïghi,Teresa Serrano-Gotarredona,Jayawan H B Wijekoon,Yingxue Wang,Kwabena Boahen +19 more
TL;DR: The most common building blocks and techniques used to implement these circuits, and an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models.
Journal ArticleDOI
A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor
TL;DR: This paper presents a dynamic and active pixel vision sensor (DAVIS) which addresses this deficiency by outputting asynchronous DVS events and synchronous global shutter frames concurrently.
Proceedings ArticleDOI
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
TL;DR: In this paper, a set of optimization techniques to minimize performance loss in the conversion process for convolutional networks and fully connected deep networks are presented, which yield networks that outperform all previous SNNs on the MNIST database.
Journal ArticleDOI
Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.
Bodo Rueckauer,Iulia-Alexandra Lungu,Yuhuang Hu,Michael Pfeiffer,Michael Pfeiffer,Shih-Chii Liu +5 more
TL;DR: This paper shows conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset.
Journal ArticleDOI
Memory and Information Processing in Neuromorphic Systems
Giacomo Indiveri,Shih-Chii Liu +1 more
TL;DR: A survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks is presented and the advantages and challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems are presented.