2 citations · 2 across the 3 of their papers we have counts for
5 papers
Training slow silicon neurons to control extremely fast robots with spiking reinforcement learning
Irene Ambrosini, Ingo Blakowski, Dmitrii Zendrikov +5
Air hockey demands split-second decisions at high puck velocities, a challenge we address with a compact network of spiking neurons running on a mixed-signal analog/digital neuromo…
ElfCore: A 28nm Neural Processor Enabling Dynamic Structured Sparse Training and Online Self-Supervised Learning with Activity-Dependent Weight Update
Zhe Su, Giacomo Indiveri
In this paper, we present ElfCore, a 28nm digital spiking neural network processor tailored for event-driven sensory signal processing. ElfCore is the first to efficiently integrat…
A neuromorphic continuous soil monitoring system for precision irrigation
Mirco Tincani, Khaled Kerouch, Umberto Garlando +4
Sensory processing at the edge requires ultra-low power stand-alone computing technologies. This is particularly true for modern agriculture and precision irrigation systems which…
Waves and symbols in neuromorphic hardware: from analog signal processing to digital computing on the same computational substrate
Dmitrii Zendrikov, Alessio Franci, Giacomo Indiveri
Neural systems use the same underlying computational substrate to carry out analog filtering and signal processing operations, as well as discrete symbol manipulation and digital c…
An Efficient Multicast Addressing Encoding Scheme for Multi-Core Neuromorphic Processors
Zhe Su, Aron Bencsik, Giacomo Indiveri +1
Multi-core neuromorphic processors are becoming increasingly significant due to their energy-efficient local computing and scalable modular architecture, particularly for event-bas…