6 papers
Inertial Asynchronous Computation
Doruk Efe Gökmen, Michel Fruchart, Dmitrii Zendrikov +3
Computation is the controlled evolution of a state. Asynchronous evolutions, where all parts of the state change in their own time without stopping each other, put this control in…
Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks
Jonathan Haag, Christian Metzner, Dmitrii Zendrikov +4
On-chip learning is key to scalable and adaptive neuromorphic systems, yet existing training methods are either difficult to implement in hardware or overly restrictive. However, r…
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…
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…
Genetic Motifs as a Blueprint for Mismatch-Tolerant Neuromorphic Computing
Tommaso Boccato, Dmitrii Zendrikov, Nicola Toschi +1
Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device misma…
Neuromorphic dreaming: A pathway to efficient learning in artificial agents
Ingo Blakowski, Dmitrii Zendrikov, Cristiano Capone +1
Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI) computing platforms. Biological systems demonstrate remarkable abilities to learn comple…