most citedSkydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance

38 citations

7 papers

q-bio.NC2022★ 19 cited

Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Robin Gutzen, Giulia De Bonis, Chiara De Luca +11

Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, a…

cs.RO2022★ 10 cited

Visual Odometry with Neuromorphic Resonator Networks

Alpha Renner, Lazar Supic, Andreea Danielescu +4

Visual Odometry (VO) is a method to estimate self-motion of a mobile robot using visual sensors. Unlike odometry based on integrating differential measurements that can accumulate…

cs.CV2022★ 19 cited

Neuromorphic Visual Scene Understanding with Resonator Networks

Alpha Renner, Lazar Supic, Andreea Danielescu +5

Analyzing a visual scene by inferring the configuration of a generative model is widely considered the most flexible and generalizable approach to scene understanding. Yet, one maj…

eess.AS2022★ 23 cited

Continuous-Time Analog Filters for Audio Edge Intelligence: Review on Circuit Designs

Kwantae Kim, Shih-Chii Liu

Edge audio devices can reduce data bandwidth requirements by pre-processing input speech on the device before transmission to the cloud. As edge devices are required to ensure alwa…

cs.LG2022★ 17 cited

Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation

Nicolas Zucchet, João Sacramento

This paper reviews gradient-based techniques to solve bilevel optimization problems. Bilevel optimization is a general way to frame the learning of systems that are implicitly defi…

cs.AR2022★ 38 cited

Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance

Qinyu Chen, Chang Gao, Xinyuan Fang +1

Spiking Neural Networks (SNNs) are developed as a promising alternative to Artificial Neural networks (ANNs) due to their more realistic brain-inspired computing models. SNNs have…